Research on mine object detection algorithm based on improved YOLOv4

Hu Che; Qian Zhang; Ruijun Liu; Jun Liu

doi:10.1117/12.3004971

9 October 2023 Research on mine object detection algorithm based on improved YOLOv4

Hu Che, Qian Zhang, Ruijun Liu, Jun Liu

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 1279103 (2023) https://doi.org/10.1117/12.3004971
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

In the detection of mining vehicles and pedestrians, the classical object detection model is not suitable for micro-small hardware equipment due to its large size and large number of parameters.However, the existing lightweight models are difficult to balance detection accuracy and real-time. In this study, a lightweight detection algorithm Depthwise MobileNetv3 -YOLOv4based on improved Mobilenet-YOLO is proposed to implement the compression of the network model. At the same time, the use of deep separable convolution on the detection head improves the ability of the network to pay attention to the target feature information, Finally, the Siou activation function is used to improve the problem of slow network convergence. The experimental results show that the Depthwise MobileNetv3-YOLOv4 network parameters are reduced by 90% compared with the original network under the condition of ensuring accuracy, and compared with other network models, Depthwise MobileNetv3-YOLOv4 is more suitable for vehicle volume.

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

Citation Download Citation

Hu Che, Qian Zhang, Ruijun Liu, and Jun Liu "Research on mine object detection algorithm based on improved YOLOv4", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 1279103 (9 October 2023); https://doi.org/10.1117/12.3004971

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KEYWORDS

Object detection

Head

Target detection

Convolution

Mining

Land mines

Feature extraction

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