Content of Special Issue on Object Detection 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 Small Object Detection Algorithm Based on ATO-YOLO SU Jia, QIN Yichang, JIA Ze, WANG Jing Computer Engineering and Applications    2024, 60 (6): 68-77.   DOI: 10.3778/j.issn.1002-8331.2308-0385 Abstract （174）      PDF（pc） （795KB）（195）       Knowledge map    Save Small object detection is of great significance in the field of computer vision. However, existing methods often suffer from issues such as missed detection and false alarms when dealing with challenges like scale variation, dense object arrangement, and irregular layouts. To address these problems, ATO-YOLO, an improved version of the YOLOv5 algorithm is proposed. Firstly, this paper introduces an adaptive feature extraction (AFE) module that incorporates an attention mechanism to enhance the feature representation capability of the detection model. By dynamically adjusting the weight allocation to highlight key object features, AFE improves the accuracy and robustness of object detection tasks in various scenarios. Secondly, a triple feature fusion (TFF) mechanism is designed to effectively utilize multi-scale information by fusing feature maps from different scales, resulting in more comprehensive object features and enhanced detection performance for small objects. Lastly, an output reconstruction (ORS) module is introduced, which removes the large object detection layer and adds a small object detection layer, enabling precise localization and recognition of small objects. This module also reduces model complexity and improves detection speed compared to the original model. Experimental results demonstrate that the ATO-YOLO algorithm achieves an mAP@0.5 of 38.2% on the VisDrone dataset, a 6.1?percentage points improvement over YOLOv5, with a relative FPS increase of 4.4%. This algorithm enables fast and accurate detection of small objects. Reference | Related Articles | Metrics Select Lightweight Foggy Weather Object Detection Method Based on YOLOv5 LAI Jing’an, CHEN Ziqiang, SUN Zongwei, PEI Qingqi Computer Engineering and Applications    2024, 60 (6): 78-88.   DOI: 10.3778/j.issn.1002-8331.2308-0029 Abstract （131）      PDF（pc） （1220KB）（158）       Knowledge map    Save Aiming at the low accuracy and high model complexity of object detection algorithms in foggy scenes, a lightweight foggy object detection method based on YOLOv5 is proposed. Firstly, this paper adopts the receptive field attention module (RFAblock) to add an attention mechanism to the receptive field by interacting with the receptive field feature information to improve the feature extraction ability. Secondly, the lightweight network Slimneck is used as the neck structure to reduce the model parameters and complexity while maintaining the accuracy. The angle vector between the real frame and the predicted frame is introduced in the loss function to improve the training speed and inference accuracy. PNMS (precise non-maximum suppression) is used to improve the candidate frame selection mechanism and reduce the leakage detection rate in the case of vehicle occlusion. Finally, the experimental results are tested on the real foggy day dataset RTTS and the synthetic foggy day dataset Foggy Cityscapes, and the experimental results show that the mAP50 is improved by 4.9 and 3.5 percengtage points, respectively, compared with YOLOv5l, and the number of model parameters is only 54.6% of that of YOLOv5l. Reference | Related Articles | Metrics Select Improved Underwater Object Detection Algorithm of YOLOv7 LIANG Xiuman, LI Ran, YU Haifeng, LIU Zhendong Computer Engineering and Applications    2024, 60 (6): 89-99.   DOI: 10.3778/j.issn.1002-8331.2309-0072 Abstract （94）      PDF（pc） （787KB）（100）       Knowledge map    Save Underwater object detection is a challenging task in the process of marine exploration and development. Addressing the issue of poor underwater target detection performance in existing algorithms due to problems such as low visibility and color distortion in underwater images, an improved YOLOv7 underwater target detection algorithm is proposed with the aim of improving underwater object detection performance. Firstly, a multi-information flow fusion attention mechanism (spatial group-wise coordinated competitive attention, SGCA) is designed to solve the problem of feature loss caused by the loss of global context information of the image in the convolution process. It improves the detection accuracy of the model in the case of image blur. Additionally, the switchable atrous convolution (SAConv) module is used to replace the 3×3 convolution module in the ELAN structure to enhance the feature extraction capability of the backbone network. Secondly, Wise-IoU is used as the loss function in the prediction part, which obtains more accurate detection results by balancing model training outcomes on images of varying quality. Finally, an underwater image enhancement method based on dark channel prior (DCP) and depth transmission maps is employed to enhance the images in the underwater dataset. Experimental results show that the improved algorithm achieves a mAP of 87.3% on the self-built underwater object detection dataset, which is 3.4 percengtage points higher than that of the original YOLOv7 algorithm. On the enhanced underwater image dataset, mAP is 87.1%, increases 2.1 percentage points. Therefore, the proposed approach exhibits superior performance in underwater object detection tasks. Reference | Related Articles | Metrics Select Improved YOLOv8 Small Target Detection Algorithm in Aerial Images FU Jinyi, ZHANG Zijia, SUN Wei, ZOU Kaixin Computer Engineering and Applications    2024, 60 (6): 100-109.   DOI: 10.3778/j.issn.1002-8331.2311-0281 Abstract （137）      PDF（pc） （771KB）（145）       Knowledge map    Save In aerial image detection task, object and the overall image size are small, scales have different characteristics and detail information is not clear, it can cause leak and mistakenly identified problems, an improved small target detection algorithm CA-YOLOv8 is proposed. Channel feature partial convolution (CFPConv) is designed. Based on this, it reconstructs a Bottleneck structure in C2f, which is named CFP_C2f. In this way, some C2f modules in YOLOv8 head and neck are replaced, the effective channel feature weights are enhanced, and the ability to obtain multi-scale detail features is improved. A context aggregated module (CAM) is embedded to improve the context aggregation ability, optimize the response of feature channels, and strengthen the ability to perceive the details of deep features. The NWD loss function is added and combined with CIoU as a positioning regression loss function to reduce the sensitivity of position bias. By making full use of the advantages of multiple attention mechanism, the original detection head is replaced with DyHead (dynamic head). In the experiment of VisDrone2019 dataset, the improved algorithm reduces the number of parameters by 33.3% compared with the original YOLOv8s model, and the detection accuracy of mAP50 and mAP50：95 increases by 8.7 and 5.7 percentage points respectively, showing good performance and confirming its effectiveness. Reference | Related Articles | Metrics Select Vehicle Detection Algorithm Based on Improved YOLOv8 in Traffic Surveillance ZHOU Fei, GUO Dudu, WANG Yang, WANG Qingqing, QIN Yin, YANG Zhuomin, HE Haijun Computer Engineering and Applications    2024, 60 (6): 110-120.   DOI: 10.3778/j.issn.1002-8331.2310-0101 Abstract （135）      PDF（pc） （817KB）（146）       Knowledge map    Save To address the current problems of insufficient vehicle detection accuracy and slow detection speed in complex traffic monitoring scenarios, a lightweight vehicle detection algorithm based on YOLOv8 model is proposed. Firstly, FasterNet is used to replace the backbone feature extraction network of YOLOv8, which reduces redundant computation and memory access, and improves the detection accuracy and inference speed of the model.Secondly, the SimAM attention module is added to the Backbone and Neck sections, which enhances the important features of the target vehicles without increasing the original network parameters, and improves the feature fusion capability. Then, to address the problem of poor detection of small-sized vehicles under dense traffic flow, a small target detection head is added to better capture the features and contextual information of small-sized vehicles. Finally, Wise-IoU, which can adaptively adjust the weight coefficients, is used as the loss function of the improved model, which enhances the regression performance of the bounding box and the robustness of the detection.The experimental results on the UA-DETRAC dataset show that compared with the original model, the improved method in this paper is able to achieve better detection accuracy and speed in the traffic monitoring system, with the mAP and FPS improved by 3.06 percengtage points and 3.36%, respectively, which effectively improves the problem of the poor detection of small-target vehicles in the complex traffic scenarios, and achieves a good balance between accuracy and speed. Reference | Related Articles | Metrics Select Improved YOLOv7-tiny UAV Target Detection Algorithm YANG Yonggang, XIE Ruifu, GONG Zechuan Computer Engineering and Applications    2024, 60 (6): 121-129.   DOI: 10.3778/j.issn.1002-8331.2307-0316 Abstract （97）      PDF（pc） （724KB）（99）       Knowledge map    Save To solve the problems that small targets are difficult to detect, dense targets and complex environment lead to the increase of missed detection probability in UAV perspective, an improved YOLOv7-tiny UAV target detection algorithm is proposed. Firstly, a parallel network is added on the basis of the backbone network to enhance the capability of extracting feature map information. Secondly, the sampling scale of small targets is increased and the FPN structure is improved, so that the feature map output of the backbone network can be used for subsequent up-sampling and down-sampling, and the network accuracy is improved. Then, coordinate attention (CA) is added to optimize the output feature map of backbone network and reduce the loss of feature information. Finally, WIoU loss function is used to calculate location loss, which enhances the detection ability of small targets. Experimental results show that compared with the original algorithm, improved YOLOv7-tiny algorithm accuracy and recall rate increased by 2.8 and 2.7 percentage points respectively, mAP@0.5 and mAP@0.5:0.95 increased by 3.8 and 3.2 percentage points respectively, effectively improve the detection accuracy of the algorithm. Reference | Related Articles | Metrics Select Improved YOLOv5 Helmet Wearing Detection Algorithm for Small Targets DENG Zhenrong, XIONG Yuxu, YANG Rui, CHEN Yuren Computer Engineering and Applications    2024, 60 (3): 78-87.   DOI: 10.3778/j.issn.1002-8331.2305-0209 Abstract （80）      PDF（pc） （702KB）（96）       Knowledge map    Save Safety helmets are the safety guarantee for construction personnel, but existing safety helmet detection models have issues such as false detection and missed detection of overlapping and dense small targets in complex environments. Therefore, an improved small target detection algorithm of YOLOv5 is proposed. Transformer is added to the backbone network of YOLOv5 to capture global information at multiple scales and obtain richer high-level semantic features. This paper uses GsConv convolution for feature fusion enhancement and introduces coordinate attention mechanism to enable the network to pay attention on a larger area. The detection head decouples classification and regression to accelerate convergence speed. Anchor-free detection method is used to simplify algorithm structure and accelerate detection speed. The EIOU loss function is used to optimize the accuracy of frame prediction. The experimental results on the self-made helmet dataset show that the improved YOLOv5 model has an average accuracy of 96.33%, which is 4.73?percentage points higher than the YOLOv5 model, meeting the requirements for detecting overlapping and dense small targets under complex conditions. Reference | Related Articles | Metrics Select Improved Lightweight Underwater Target Detection Algorithm of YOLOv7 XIN Shi’ao, GE Haibo, YUAN Hao, YANG Yudi , YAO Yang Computer Engineering and Applications    2024, 60 (3): 88-99.   DOI: 10.3778/j.issn.1002-8331.2308-0333 Abstract （75）      PDF（pc） （776KB）（85）       Knowledge map    Save Aiming at the problems of target false and missing detection caused by limited memory and computing power of underwater equipment and complex underwater environment, a lightweight underwater target detection method YOLOv7-SDBB is proposed. The ShuffleNetv2 lightweight network is introduced on the backbone network of YOLOv7 to reduce the parameter amount and calculation amount of the feature extraction network. The D-ELAN and D-MPConv modules are designed to further realize the network lightweight and improve the model detection speed. Due to the phenomenon of false and missing detection is prone to occur during underwater detection, BiFPN is used to perform multi-scale feature fusion and integrate deep feature information. In view of the problem of feature information loss caused by BiFPN feature fusion, the BiFormer attention mechanism is used to retain key information and improve target detection accuracy. The experimental results show that the accuracy of the improved model on the URPC2020 dataset has increased by 2.7 percentage points, the amount of parameters and calculations have decreased by 20.3% and 41.7% respectively, and the detection speed has increased to 100.9?FPS, realizing a good balance between the speed and accuracy of underwater target detection. Reference | Related Articles | Metrics Select Dense Small Object Detection Algorithm Based on Improved YOLOv5 in UAV Aerial Images CHEN Jiahui, WANG Xiaohong Computer Engineering and Applications    2024, 60 (3): 100-108.   DOI: 10.3778/j.issn.1002-8331.2306-0289 Abstract （118）      PDF（pc） （739KB）（143）       Knowledge map    Save UAV aerial images have many instances of small objects, drastic size changes and dense occlusions, etc. To solve the difficulty of existing object detection algorithms to detect small objects in aerial images, an RDS-YOLOv5 detection algorithm for dense small objects is proposed. Adding a new small object detection layer to the three detection layers of YOLOv5 to retain richer feature information, the ability of the network is enhanced to extract small object features and reduce false and miss detection. A multi-scale feature extraction module C3Res2Block with a hierarchical residual structure is designed to improve the multi-scale feature representation capability of network as well as to suppress the generation of conflicts. Decoupled detection head is used to avoid the prediction bias caused by the difference between different tasks, which improves the localization and detection accuracy. The confidence of the anchor box is optimized using the Soft NMS algorithm to improve the detection accuracy of model for dense small objects. The experimental results of VisDrone dataset show that RDS-YOLOv5 improves 12.9 percentage points on mAP0.5 and 10.6 percentage points on mAP0.5：0.95 compared with the baseline model YOLOv5, and achieves better detection accuracy compared with the current mainstream object detection algorithms, which can effectively accomplish the task of dense small object detection in UAV aerial images. Reference | Related Articles | Metrics Select Research on Lightweight Improved Algorithm for Indoor Target Detection Based on YOLOv5s NIU Xinyu, MAO Pengjun, DUAN Yuntao, LOU Xiaoheng Computer Engineering and Applications    2024, 60 (3): 109-118.   DOI: 10.3778/j.issn.1002-8331.2305-0109 Abstract （50）      PDF（pc） （1026KB）（62）       Knowledge map    Save The existing indoor target detection algorithms have many problems, such as complex structure, large amount of calculation and large number of model parameters, which are difficult to be deployed to the indoor robot platform with limited computing capacity to achieve efficient target detection. To solve this problem, an improved YOLOV5s detection algorithm is proposed. In this method, ShuffleNetv2 is introduced as the backbone feature extraction network, and CA attention mechanism is adopted on the basis of the improved backbone network, and GSConv and VOV-GSCSP modules are adopted in the neck network. Finally, the bounding regression loss function EIOU is introduced to accelerate the network convergence. The results show that the improved target detection algorithm reduces the model computation by 68.75%, the number of model parameters by 62.2%, the weight file by 59.7%, and the average accuracy mAP is 0.653. The improved target detection model can ensure the detection accuracy while ensuring the lightweight. Reference | Related Articles | Metrics Select Remote Sensing Image Target Detection Algorithm Based on Improved YOLOv6 XU Degang, WANG Zaiqing, XING Kuijie, GUO Yixin Computer Engineering and Applications    2024, 60 (3): 119-128.   DOI: 10.3778/j.issn.1002-8331.2304-0270 Abstract （83）      PDF（pc） （876KB）（61）       Knowledge map    Save Aiming at the low target detection accuracy caused by complex background of remote sensing images, generally small targets and multi-scale distribution of targets, a remote sensing image target detection algorithm based on improved YOLOv6 is proposed. Firstly, a coordinate attention module is introduced into the backbone network to improve the feature extraction ability and target location ability of the model under complex background. Secondly, a context enhancement module is proposed to enable the model to obtain rich context information, so as to improve the ability of model to extract multi-scale target details. Finally, in order to realize the adaptive fusion of different scale features, an adaptive spatial feature fusion is introduced into the neck network to improve the detection accuracy of multi-scale targets, especially small targets. The proposed algorithm is trained and tested on DOTA-v1.0, an open data set of remote sensing images. The experimental results show that the convergence speed and convergence accuracy of the improved algorithm are better than that of the original algorithm, and the AP value reaches 54.6%, which is 1.4 percentage points higher than that of the original algorithm. Meantime, compared with some other advanced target detection algorithms, accuracy and speed is improved which demonstrates the effectiveness of the improved algorithm. Reference | Related Articles | Metrics Select Improved YOLOv8 Multi-Scale and Lightweight Vehicle Object Detection Algorithm ZHANG Lifeng, TIAN Ying Computer Engineering and Applications    2024, 60 (3): 129-137.   DOI: 10.3778/j.issn.1002-8331.2309-0145 Abstract （184）      PDF（pc） （713KB）（180）       Knowledge map    Save To address issues such as high hardware requirements, low detection accuracy, and a high rate of missed overlapping targets in traditional vehicle object detection models, a modified vehicle object detection algorithm called RBT-YOLO based on YOLOv8 is proposed. The main network is reconstructed using a multi-scale fusion approach. BiFPN is improved by adding convolutional operations and adjusting input/output channel numbers to adapt to YOLOv8, enhancing its feature fusion capability. After the feature maps are output from the Neck section, a lightweight attention mechanism called Triplet Attention is introduced to enhance the feature extraction ability of the model. To address the issue of high target overlap in real scenarios, SoftNMS (soft non-maximum suppression) is used to replace the original NMS, making the model to handle the candidate boxes more gentle, thereby strengthening detection capabilities of the model and improving recall rates. Experimental results on the Pascal VOC and MS COCO datasets demonstrate that the proposed RBT-YOLO outperforms the original model, reducing parameters and computations by approximately 60%, the mAP improved by 2.6 and 3.0 percentage points, and excelling in both size and precision compared to other classic detection models, thus demonstrating strong practical utility. Reference | Related Articles | Metrics