Content of 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 Research on Improved YOLOv5 Algorithm and Its Application in Multi-Object Detection for Automatic Driving SONG Shaojian, XIA Haijie, LI Gang Computer Engineering and Applications    2023, 59 (15): 68-75.   DOI: 10.3778/j.issn.1002-8331.2302-0185 Abstract （182）      PDF（pc） （644KB）（130）       Knowledge map    Save Real-time detection of vehicles, pedestrians, and other traffic participants is an important part of information interaction between the autonomous vehicle and the external environment. However, the low accuracy of multi-target detection in complex weather conditions is still a challenge. For this reason, this paper proposes an improved algorithm of YOLOv5 and its application in the multi-target detection of auto-driving systems. In this method, K-means++ algorithm is used to cluster the target samples in the data set to obtain anchor frames that are more suitable for different target scales and improve the accuracy of multi-target location and entity segmentation. Coordinate attention module is added to the backbone of the original YOLOv5 to improve the feature extraction capability of the model. The PANet（path aggregation network） structure in the original YOLOv5 network is replaced by the BiFPN（bidirectional feature pyramid） structure to realize the bidirectional fusion of deep and shallow features from top to bottom and from bottom to top, and improves the overall detection accuracy of the algorithm for targets with different scales. The experimental results show that the improved YOLOv5 algorithm achieves better performance, and the mAP of target detection reaches 92.2%, which is 8.47% higher than the results obtained by original YOLOv5 algorithm. Reference | Related Articles | Metrics Select Improved YOLOv5 Object Detection Algorithm for Remote Sensing Images YANG Chen, SHE Lu, YANG Lu, FENG Zixian Computer Engineering and Applications    2023, 59 (15): 76-86.   DOI: 10.3778/j.issn.1002-8331.2301-0220 Abstract （165）      PDF（pc） （847KB）（113）       Knowledge map    Save An improved YOLOv5 is proposed to address complex backgrounds and small objects missing detection in remote sensing images. Firstly, considering that the high-level feature map contains little small object information caused by down-sampling of convolutional neural networks, low-level feature is reused to increase the small target feature information. The EMFFN（efficient multi-scale feature fusion network） is used in the feature fusion stage instead of the original PANet（path aggregation network） to efficiently fuse the feature map information at different scales by adding jump connections and skip connections. Finally, a bidirectional feature attention mechanism（BFAM） including channels attention and pixel attention is designed to improve detection in complex background. To evaluate the proposed model, this paper uses two remote sensing image datasets, DIOR and RSOD. The experimental results show that the improved YOLOv5 model achieves 87.8% and 96.6% detection accuracy in the DIOR and RSOD datasets respectively, which is 5.2 and 1.6?percentage points better than the original YOLOv5 algorithm, effectively improving the detection accuracy of small targets in complex backgrounds. Reference | Related Articles | Metrics Select Road Object Detection Method for Complex Road Scenes SHENG Boying, HOU Jin, LI Jiaxin, DANG Hui Computer Engineering and Applications    2023, 59 (15): 87-96.   DOI: 10.3778/j.issn.1002-8331.2212-0093 Abstract （113）      PDF（pc） （758KB）（70）       Knowledge map    Save Aiming at the problem of low detection accuracy of small-scale targets  in complex traffic scenes, and prone to false detection and missed detection, a target detection algorithm YOLOv5s-MRS based on YOLOv5s is proposed. Firstly, a feature extraction network（RFP-PAN） based on feedback mechanism is proposed to increase the shallow feature layer with feedback connection and design the IASPP module to fully fuse the feature information of different scales. Secondly, the cascaded attention mechanism（SECA） is proposed to focus on important features in channel and spatial dimensions and make the model to focus on more useful information. Finally, Ghost module is used to reduce the number of parameters, computation and model occupation space of the model. The experimental results show that the detection accuracy of YOLOv5s-MRS reaches 93.4% and 40.8% on KITTI dataset and VisDrone2021 DET dataset, respectively, which is 1.6 and 8.6 percentage points higher than that of the original algorithm and the model size is 12.9 MB. YOLOv5s-MRS has good detection accuracy while ensuring real-time, and  solves the problem of missing and false detection of small targets to some extent. Reference | Related Articles | Metrics Select Lightweight Intra-School Pedestrian Detection Algorithm Based on Improved YOLOv4-Tiny SUN Hao, DONG Xingfa, WANG Jun, CHEN Zhiyuan Computer Engineering and Applications    2023, 59 (15): 97-106.   DOI: 10.3778/j.issn.1002-8331.2207-0313 Abstract （76）      PDF（pc） （805KB）（69）       Knowledge map    Save Deep learning is often used for pedestrian detection. In order to apply complex traditional convolutional neural networks on embedded devices, the lightweight of the network is an inevitable trend, but it is difficult to balance speed and accuracy. To solve this problem, this paper designs a lightweight intra-school pedestrian target detection algorithm based on improved YOLOv4-Tiny. Firstly, an improved Ghost convolution feature extraction module is proposed, which is a multi-scale hole convolution module. At the same time, ordinary convolution is replaced by depthwise separable convolution, which reduces the complexity of the model and increases the diversity of feature extraction. Secondly, an improved spatial pyramid pooling structure with depthwise separable convolution of holes enhances the fusion of contextual features, improves detection accuracy, and reduces network parameters. Finally, Soft-NMS is introduced to replace traditional non-maximum suppression to reduce the missed detection rate. Experimental results show that the algorithm has the characteristics of high accuracy, fast speed, few model parameters and small size on multiple data sets and hardware platforms, and can be applied to embedded devices. Reference | Related Articles | Metrics Select UAV Image Small Object Detection on Complex Background WANG Xiaohong, HU Yu Computer Engineering and Applications    2023, 59 (15): 107-114.   DOI: 10.3778/j.issn.1002-8331.2303-0154 Abstract （142）      PDF（pc） （839KB）（92）       Knowledge map    Save Algorithm for small object detection, called EMT-ECoTNet, has been proposed. It is based on the improved YOLOv7-w6 and aims to address the issue of low detection accuracy resulting from complex backgrounds and small object features in UAV images. The ECoT Block is used to construct the algorithm, which consists of CoT modules with global modeling advantages and MA-ECA channel attention modules. This block is beneficial for small object feature extraction by increasing the maximum pooling layer MaxPool to extract more texture information from small object. Additionally, the M-SPPFCSPC, which has a large receptive field, is used to further enhance the small object features. The EIoU loss function is used to penalize the predicted width and height between the predicted and ground truth boxes, which helps to improve the convergence speed and accuracy. The experimental results demonstrate that EMT-ECoTNet achieves an mAP50 of 62.8% on the VisDrone dataset, which is 3.2?percentage points higher than the original baseline model YOLOv7-w6. Furthermore, it has better detection performance than mainstream algorithms in UAV small object detection tasks. Reference | Related Articles | Metrics