Optimization of detection algorithm based on YOLOV5

Penglei Zhang

doi:10.1117/12.3008238

14 November 2023 Optimization of detection algorithm based on YOLOV5

Penglei Zhang

Proceedings Volume 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023); 129341R (2023) https://doi.org/10.1117/12.3008238
Event: 2023 3rd International Conference on Computer Graphics, Image and Virtualization (ICCGIV 2023), 2023, Nanjing, China

Abstract

Aiming at the problems that the current YOLOV5 target detection algorithm has many parameters, complex network structure, and high configuration required for training models, CNS-YOLO proposes a lightweight algorithm based on YOLOV5 (CNS-YOLO). First of all, this paper uses the ConvNeXt structure to optimize the neck network of YOLOV5, and secondly, this paper uses Shufflenetv2 to improve the backbone structure of YOLOV5, and reconstructs the feature extraction network and feature fusion network. Through the improvement of these two structures, the problem of too many parameters of YOLOV5 is optimized, and finally the group convolution and attention mechanism are added to further increase the ability to extract information and suppress background noise, thereby improving the detection speed and detection accuracy of the algorithm. The results of the RSOD data set show that the mAP@0.5 of the CNS-YOLO network has increased by 2.3 percentage points compared to before the improvement, and the FLOPs have decreased by 12.8G compared to the before improvement; the generated model file has decreased by 10.6M compared to before. In the case of reducing the number of model parameters, mAP@0.5 is still improved, indicating that the algorithm has achieved good improvements in all aspects and improved the effect of target detection.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Penglei Zhang "Optimization of detection algorithm based on YOLOV5", Proc. SPIE 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023), 129341R (14 November 2023); https://doi.org/10.1117/12.3008238

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KEYWORDS

Convolution

Mathematical optimization

Detection and tracking algorithms

Object detection

Feature extraction

Neck

Target detection

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