Content of Special Issue on YOLOv8 Improvements and Applications in our journal

Published in last 1 year |  In last 2 years |  In last 3 years |  All Please wait a minute... For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails Select LF-YOLO for Strip Surface Defect Detection in Industrial Scenes MA Xiaoyao, LI Rui, LI Zili, ZHAI Wenzheng Computer Engineering and Applications    2024, 60 (18): 78-87.   DOI: 10.3778/j.issn.1002-8331.2404-0411 Abstract （11）      PDF（pc） （4872KB）（17）       Knowledge map    Save Aiming at the problem of low accuracy of traditional defect detection algorithms in practical applications due to the small size of strip surface defects and blurry collected images in industrial scenarios, an LF-YOLO algorithm for strip surface defect detection in industrial scenarios is proposed. The model upsamples the input pixels by designing a local filling upsampling module to improve the  recognition ability of blurred images, and reduce the  missed detection rate of small target defects. The FReLU activation function that focuses on visual tasks is introduced to improve the accuracy of model location defects. In addition, a lightweight local attention mechanism is proposed and combined with the feature extraction module C2f to enhance the feature extraction capability of defects of different sizes during the feature extraction process of the model. Experimental results on the Northeastern University open source strip steel dataset NEU-DET and GC10-DET show that the average detection accuracy of the improved model is 7.0 and 15.4 percentage points higher than the accuracy of the original YOLOv8 algorithm, and is better than other classic target detection models. It has advantages in average detection accuracy, and the validity of each module is further verified through ablation experiments. Reference | Related Articles | Metrics Select Lightweight Detection of Ceramic Tile Surface Defects on Improved YOLOv8 YU Songsen, XUE Guopeng, HE Huang, ZHAO Gui, WEN Huosheng Computer Engineering and Applications    2024, 60 (18): 88-102.   DOI: 10.3778/j.issn.1002-8331.2312-0155 Abstract （13）      PDF（pc） （8560KB）（10）       Knowledge map    Save In terms of tile surface defect detection, under the premise of ensuring a certain detection speed, it is more difficult to detect small target defects, and the overall detection accuracy is still low. This paper proposes an improved tile surface defect detection method for YOLOv8. Firstly, data preprocessing is performed on the original large-format tile dataset, and tile data suitable for the input size of YOLOv8 is obtained through slicing operation to prevent tile defects from being lost in the process of scaling. Secondly, taking into account that there is a large proportion of small target defects on the tile surface, the structure of SPD-Conv is used instead of the traditional downsampling method, which can completely retain all the information in the channel dimension, so as to improve the detection ability of small target defects. Thirdly, the original C2f module in YOLOv8 is modified by adding the efficient channel attention (ECA) mechanism, designing the C2f_ECA module, and replacing it in the backbone network, so that the network can pay more attention to the defect information and reduce the interference of background information in the process of feature extraction  Fourthly, the tiny target detection head is added to detect after the second downsampling to improve the detection ability of YOLOv8 on tiny targets. The method is experimentally validated on the Tianchi tile defect detection dataset, and the improved model achieves 57.7%, 86.6%, and 60.6% on mAP50-95, mAP50, and mAP75, respectively, which are 9.4, 5, and 14.3 percentage points higher than the base network YOLOv8s, respectively. Meanwhile, there are higher accuracy and much lower complexity than YOLOv8m, which is a lightweight model and meets the needs of industrialization. Reference | Related Articles | Metrics Select RCSA-YOLO: Improved SAR Ship Instance Segmentation of YOLOv8 WANG Lei, ZHANG Bin, WU Qihong Computer Engineering and Applications    2024, 60 (18): 103-113.   DOI: 10.3778/j.issn.1002-8331.2401-0445 Abstract （9）      PDF（pc） （4659KB）（13）       Knowledge map    Save Addressing the challenges of low segmentation accuracy in synthetic aperture radar (SAR) images due to complex backgrounds, small targets, and large scale variations, the RCSA-YOLO algorithm is proposed. Firstly, the structural reparameterization technique is used to design the RepBlock feature extraction module, which replaces the original C2f module in the network. This improves the  ability of  network to extract and represent features, effectively filtering out interference from complex background noise. Secondly, the content-aware reassembly of features (CARAFE) module replaces the nearest neighbor upsampling method, reducing the loss of information in small targets and improving segmentation refinement. Finally, the switchable atrous convolution (SAC) is used for downsampling operations, allowing for the dynamic adjustment of receptive field size. This gives the model better adaptability to multiple scales and ensures segmentation accuracy for different sizes of vessels. Experimental evaluation on the HRSID dataset demonstrates that the proposed algorithm increases the AP50 value of the YOLOv8 model from 87.7% to 90.7%, surpassing the original algorithm by 3 percentage points. Comparative analysis against mainstream instance segmentation algorithms further reveals significant enhancement in SAR ship instance segmentation accuracy, validating the effectiveness of RCSA-YOLO. Reference | Related Articles | Metrics Select Improved Lightweight Military Aircraft Detection Algorithm of YOLOv8 LIU Li, ZHANG Shuo, BAI Yu’ang, LI Yujian, ZHANG Chuxia Computer Engineering and Applications    2024, 60 (18): 114-125.   DOI: 10.3778/j.issn.1002-8331.2404-0058 Abstract （14）      PDF（pc） （5596KB）（8）       Knowledge map    Save Military aircraft detection with remote sensing images is of great significance in the fields of reconnaissance and early warning, intelligence analysis and so on. In order to make the military aircraft inspection model run efficiently on the equipment with limited computing power, the lightweight improvement of YOLOv8n is carried out from two aspects: network design and model compression. In the aspect of network design, firstly, FAS_C2f is used to replace the C2f module in the original backbone network, which reduces the computational redundancy and speeds up the network feature extraction. Secondly, the network structure is optimized according to the scale characteristics of military aircraft targets to alleviate the problem of small target information loss caused by excessive downsampling. Thirdly, Inner-SIoU is used as a new localization regression loss function to improve the learning ability of small target samples and accelerate the convergence of regression bounding box. In terms of model compression, channel pruning based on LAMP fraction is used to compress the redesigned model to further reduce parameters and model size. With channel-wise knowledge distillation (CWD), the accuracy of the model is restored to the level close to that before pruning. The experimental results show that on the open military aircraft data set MAR20, the mAP of the lightweight model is 97.2%, the volume is only 0.7 MB, which is 88.3% smaller than the original model, and the FPS is increased by 14 frames per second, which meets the real-time requirements of military aircraft target detection. Reference | Related Articles | Metrics Select Research on O-Ring Surface Defect Detection Algorithm Based on Improved YOLOv8n LI Qi, SHI Yan, FAN Tao Computer Engineering and Applications    2024, 60 (18): 126-135.   DOI: 10.3778/j.issn.1002-8331.2405-0099 Abstract （7）      PDF（pc） （7002KB）（8）       Knowledge map    Save Aiming at the problems such as small surface defect scale, high similarity between defect features and background, difficulty in feature extraction and the needs of industrial real-time detection, an improved YOLOv8n YOLO-Oring algorithm is proposed. On the basis of convolution calculation, a new CBLGhost module is designed by incorporating linear changes to reduce the computational resources required for calculating feature graphs. In view of the high similarity between defects and background, the DySample dynamic up-sampling module is introduced to make the sampling points concentrate in the target area and ignore the background part, so as to realize the effective identification of defects. In order to improve the detection efficiency, the C2f-OREPA module is designed to convert the complex structural reparameters into a single convolution layer, which can reduce a lot of training time while maintaining the feature expression ability. In order to improve the  ability of algorithm to identify small-scale defects, DyHead-DCNv3 detection head is designed to identify multi-scale targets, and make up for the shortcomings of traditional standard convolution in long-distance modeling ability and adaptive spatial aggregation ability, so as to better complete the detection task. Due to the lack of O-ring detection dataset, 1?734 datasets including 3 types of defects, such as scratches, dents and burrs, are established. Experimental results show that the mAP of YOLO-Oring algorithm reaches 94.1%, increasing by 1.3 percentage points, and FLOPs has decreased by 16%. Compared with mainstream target detection algorithms, the results show that YOLO-Oring algorithm has better detection performance for O-ring surface defects and is more conducive to industrial real-time detection. Reference | Related Articles | Metrics Select Improved YOLOv8 Urban Vehicle Target Detection Algorithm XU Degang, WANG Shuangchen, WANG Zaiqing, YIN Kedong Computer Engineering and Applications    2024, 60 (18): 136-146.   DOI: 10.3778/j.issn.1002-8331.2401-0277 Abstract （18）      PDF（pc） （6421KB）（16）       Knowledge map    Save Aiming to address the challenges of missing detection, low precision, and weak generalization ability in urban vehicle target detection algorithms for complex traffic scenes, an enhanced YOLOv8 algorithm is proposed. Firstly, this paper replaces the C2f module in the backbone network with an improved GAM-C2f structure to strike a balance between computational efficiency and model accuracy. Secondly, a SPPFAPGC module is designed to prevent local feature loss caused by maximum pooling operations in the SPPF structure. This enhances the richness of the feature map and combines it with a small target detection head to strengthen distant small target vehicle detection capability while integrating local and global features effectively. Finally, to suppress harmful gradients generated by low-quality images, this paper utilizes WIOU loss function instead of CIoU for improved bounding box regression performance, faster convergence speed, and higher regression accuracy. Experimental results on street vehicle datasets demonstrate that compared to the benchmark model YOLOv8n, the improved algorithm achieves a 1.6 percentage points increase in mAP50 and a 2.0 percentage points increase in Recall respectively , the problem of poor detection performance for small-target vehicles in urban traffic scenes is effectively improved. Verification on VisDrone2019 dataset also shows improvements of 1.1 percentage points in mAP50 and 1.6 percentage points in Recall further confirming the superiority of the enhanced algorithm over others mainstream algorithms regarding accuracy and recall rate specifically tailored for urban vehicle detection tasks. Reference | Related Articles | Metrics Select Algorithm for Real-Time Vehicle Detection from UAVs Based on Optimizing and Improving YOLOv8 SHI Tao, CUI Jie, LI Song Computer Engineering and Applications    2024, 60 (9): 79-89.   DOI: 10.3778/j.issn.1002-8331.2312-0291 Abstract （242）      PDF（pc） （4614KB）（319）       Knowledge map    Save To address the problems of low accuracy, easy interference from background environment and difficulty in detecting small target vehicles of existing UAV vehicle detection algorithms, an improved UAV vehicle detection algorithm YOLOv8-CX is proposed based on YOLOv8. By integrating the advantages of Deformable Convolutional Networks v1-3, a C2f-DCN module is proposed to flexibly sample features and better extract features between vehicles of different sizes. Utilizing the idea of large separable kernel attention, a SPPF-LSKA module is proposed with long-range dependency and self-adaptability, which can effectively reduce background interference on vehicle detection. In the neck network, a CF-FPN (ment network for tiny object deteciton) feature fusion structure is adopted to enhance the detection accuracy of small targets by combining contextual information and suppressing conflicts between features at different scales. Finally, the original YOLOv8 head is replaced with a Dynamic Head detection head. By unifying scale, space and task, the three types of attention mechanisms, the model detection performance is further improved. Experimental results show that on the Mapsai dataset, compared with the original algorithm, the improved algorithm increases the accuracy (P), recall (R) and mean average precision (mAP) by 8.5, 11.2 and 6.2 percentage points respectively, and the algorithm detection speed reaches 72.6 FPS, meeting the real-time requirements of UAV vehicle detection. By comparing with other mainstream target detection algorithms, the effectiveness and superiority of this method are validated. Reference | Related Articles | Metrics Select Small Sample Steel Plate Defect Detection Algorithm of Lightweight YOLOv8 DOU Zhi, GAO Haoran, LIU Guoqi, CHANG Baofang Computer Engineering and Applications    2024, 60 (9): 90-100.   DOI: 10.3778/j.issn.1002-8331.2311-0070 Abstract （247）      PDF（pc） （5010KB）（311）       Knowledge map    Save The surface area of steel plate is large, and the surface defects are very common, and showing the characteristics of multi-class and small amount. Deep learning is difficult to be effectively applied to the detection of such small sample defects. In order to solve this problem, a small sample steel plate defect detection algorithm based on lightweight YOLOv8 is proposed. Firstly, an interactive data augmentation algorithm based on fuzzy search is proposed, which can effectively solve the problem that the network model cannot be effectively trained due to the lack of training samples, making it possible for deep learning to be applied in this field. Then, the LMRNet (lightweight multi-scale residual networks) network is designed to replace the backbone of YOLOv8, to achieve the lightweight of the network model and improve its portability. Finally, the CBFPN (context bidirectional feature pyramid network) and ECSA (efficient channel spatial attention) modules are proposed to make the network more effective in extracting and fusing scar features, and the Wise-IoU loss function is adopted to improve the detection performance. The comparative experimental results show that compared with the original YOLOv8 algorithm, the amount of parameters of the improved network is only 30% of the original network, the amount of calculation is 49% of the original network, the FPS is increased by 9 frame/s. The accuracy rate, recall rate and mAP have increased by 2.9, 6.5 and 5.5 percentage points respectively. Experimental results fully verify the advantages of the proposed algorithm. Reference | Related Articles | Metrics Select Improved YOLOv8 Method for Anomaly Behavior Detection with Multi-Scale Fusion and FMB SHI Yangyu, ZUO Jing, XIE Chengjie, ZHENG Diwen, LU Shuhua Computer Engineering and Applications    2024, 60 (9): 101-110.   DOI: 10.3778/j.issn.1002-8331.2401-0240 Abstract （124）      PDF（pc） （7946KB）（169）       Knowledge map    Save To resolve the problems of anomaly behavior detection including multi-scale variations, miss and false detection, and complex background interference, a method is proposed by incorporating the fusion of multi-scale features and fast multi-cross block (FMB) for anomaly behavior detection. Based on YOLOv8 as the baseline network, a FMB has been designed in the backbone to enhance context information awareness and reduce network parameters. Meanwhile, a spatial-progressive convolution pooling (S-PCP) module has been proposed to achieve multi-scale information fusion, thereby reducing the issues of miss and false detection caused by scale differences and improving detection accuracy. A SimAM attention mechanism has been introduced in the neck to suppress complex background interference and improve object detection performance. And WIoU has been used to balance the penalty force on anchor boxes, enhancing the model’s generalization performance. The proposed method has been extensively validated on the UCSD-Ped1 and UCSD-Ped2 datasets, and its generalization has been tested on the OPIXray dataset. The results indicate that the proposed method with fewer parameters achieves different improvements in anomaly behavior recognition accuracy compared to many advanced detection algorithms, demonstrating an excellent detection method for pedestrian anomaly behavior. Reference | Related Articles | Metrics Select Wear-YOLO：Research on Detection Methods of Safety Equipment for Power Personnel in Substations WANG Ru, LIU Daming, ZHANG Jian Computer Engineering and Applications    2024, 60 (9): 111-121.   DOI: 10.3778/j.issn.1002-8331.2308-0317 Abstract （123）      PDF（pc） （4736KB）（137）       Knowledge map    Save Aiming at the low accuracy and poor generalization of the target detection algorithm for safety equipment such as safety helmets, insulating gloves, and insulating shoes of traditional substation electric personnel, especially for the difficulty of detecting whether to wear insulating gloves or not, an improved YOLOv8 detection algorithm Wear-YOLO for substation power personnel safety equipment is proposed. In order to better learn the contextual information of complex scenes, the C2f （CSP bottleneck with 2 convolutions） module of the Backbone part of YOLOv8 is replaced with the MobileViTv3 module that integrates the Transformer structure to capture long-distance dependencies and contextual information and combine it with local information. And the feature extraction capability of the model is improved in substation scenarios. At the same time, in order to optimize the small target detection effect, a small target detection layer is introduced to enhance the  extraction of the network in shallow semantic information, thereby improving the  detection accuracy for small targets not wearing insulating gloves. WIoUv3 is used to improve the bounding box regression loss function, and a dynamic non-monotonic focusing mechanism is introduced to make the model focuses more on ordinary quality anchor boxes, thus improving the accuracy of model detection and its adaptability to complex situations. The experimental results show that the average detection accuracy is 92.1%, the detection accuracy of helmets is 96.8%, the detection accuracy of wearing insulating gloves is 92.1%, the detection accuracy of not wearing insulating gloves is 81.6%, and the detection accuracy of insulating shoes is 93.0%. Compared with the classic target detection models Faster R-CNN, SSD, RetinaNet, and YOLOv5, it has better detection accuracy and robustness. Reference | Related Articles | Metrics Select FA-SORT：Lightweight Multi-Vehicle Tracking Algorithm OUYANG Bo, ZHU Yongjian, YANG Likang, WANG Benyuan Computer Engineering and Applications    2024, 60 (9): 122-134.   DOI: 10.3778/j.issn.1002-8331.2307-0359 Abstract （101）      PDF（pc） （5812KB）（128）       Knowledge map    Save In recent years, UAVs have been widely used in the field of vehicle tracking due to their small size and flexibility. However, when UAVs fly at high altitude, there are few pixel points, crowding, and occlusion of vehicle objects in their captured images. Moreover, existing multi-object tracking research methods use Kalman filter prediction when nonlinear motion occurs during vehicle occlusion, and the problem of inaccurate vehicle position prediction occurs. In order to solve these problems, this paper adopts tracking by detection (TBD) paradigm, which firstly improves the YOLOv8 detection algorithm by introducing BiFormer sparse dynamic attention module in the network structure for extracting small object feature information. Meanwhile, the lightweight upsampling operator CARAFE is used to replace the original nearest-neighbor interpolation upsampling, which reduces the problem of small-object feature loss in the upsampling process. Then a lightweight tracking model FA-SORT is proposed, and three improvements are proposed for the SORT algorithm：improving KF, adding speed-direction consistency matching and detection value matching. Finally, the improved YOLOv8 algorithm is validated on a homemade combination of several vehicle datasets. The experimental results show that the precision is improved by 0.97% and the recall is improved by 0.898% compared with YOLOv8. The proposed FA-SORT algorithm is validated using the UAVDT dataset, and the results show that the first HOTA metric reaches 70.05%, IDF1 reaches 87.45%, and the tracking speed reaches 29.93 FPS compared to existing multi-objective tracking algorithms. The superiority of the FA-SORT tracking algorithm for multi-vehicle tracking tasks is verified. Reference | Related Articles | Metrics Select Low-Light Object Detection Combining Transformer and Dynamic Feature Fusion CAI Teng, CHEN Cifa, DONG Fangmin Computer Engineering and Applications    2024, 60 (9): 135-141.   DOI: 10.3778/j.issn.1002-8331.2310-0131 Abstract （126）      PDF（pc） （3952KB）（153）       Knowledge map    Save To address the issues of high parameter and computational complexity, poor real-time performance, and limited applicability to mobile devices in existing low-light object detection algorithms, this paper proposes an improved lightweight model called DarkYOLOv8 based on YOLOv8 for low-light object detection. Firstly, MobileNet v2 is replaced the backbone network of YOLOv8 to enhance the  feature extraction capabilities of the model. Secondly, the Transformer attention mechanism is utilized to capture global information from the images and the Transformer module parameters are trained based on target annotation information as labels to enhance the weights within the target regions, thereby improving the capability of the model to extract target features under low-light conditions. Finally, the dynamic feature fusion attention (DFFA) module is employed for feature fusion in the neck network, dynamically fusing shallow and deep features, simultaneously, the YOLOv8X algorithm is employed in combination with CBAM to supervise the training of spatial attention weights in the CBAM module of DFFA. The experimental results show that on the ExDark dataset, DarkYOLOv8 achieves 70.1% on the mAP50 metric with only 8.53 GFLOPs, which is a 3.9 percentage points improvement compared to YOLOv8n. Reference | Related Articles | Metrics Select Improved YOLOv8s Model for Small Object Detection from Perspective of Drones PAN Wei, WEI Chao, QIAN Chunyu, YANG Zhe Computer Engineering and Applications    2024, 60 (9): 142-150.   DOI: 10.3778/j.issn.1002-8331.2312-0043 Abstract （316）      PDF（pc） （5858KB）（445）       Knowledge map    Save Facing with the problems of small and densely distributed image targets, uneven class distribution, and model size limitation of hardware conditions, object detection from the perspective of drones has less precise results. A new improved model based on YOLOv8s with multiple attention mechanisms is proposed. To solve the problem of shared attention weight parameters in receptive field features and enhance feature extraction ability, receptive field attention convolution and CBAM (concentration based attention module) attention mechanism are introduced into the backbone, adding attention weight in channel and spatial dimensions. By introducing large separable kernel attention into feature pyramid pooling layers, information fusion between different levels of features is increased. The feature layers with rich semantic information of small targets are added to improve the neck structure. The inner-IoU loss function is used to improve the MPDIoU (minimum point distance based IoU) function and the inner-MPDIoU instead of the original loss function is used to enhance the learning ability for difficult samples. The experimental results show that the improved YOLOv8s model has improved mAP, P, and R by 16.1%, 9.3%, and 14.9% respectively on the VisDrone dataset, surpassing YOLOv8m in performance and can be effectively applied to unmanned aerial vehicle visual detection tasks. Reference | Related Articles | Metrics Select Baggage Tracking Technology Based on Improved YOLO v8 CAO Chao, GU Xingsheng Computer Engineering and Applications    2024, 60 (9): 151-158.   DOI: 10.3778/j.issn.1002-8331.2310-0238 Abstract （182）      PDF（pc） （6479KB）（277）       Knowledge map    Save In the airport baggage sorting scenario, the traditional multi-target tracking algorithm has the problems of high target ID switching rate and high false alarm rate of target trajectory. This paper presents a baggage tracking technique based on improved YOLO v8 and ByteTrack algorithms. The CBATM module is added, the ADH decoupling head is replaced and the loss function during training is changed, the detection accuracy is increased, the discrimination of target features is strengthened, and the ID switching rate of the target is reduced. GSI interpolation processing in Byte data association, which not only uses high box and low box, but also ensures the tracking effect after a long time of occlusion, and reduces the ID error switching caused by occlusion. In the airport baggage sorting dataset, MOTA and IDF 1 reach 89.9% and 90.3%, respectively, which show a significant improvement and can steadily realize the tracking of luggage ID. Reference | Related Articles | Metrics