YOLOv4-tiny pedestrian detection method based on attention fusion mechanism

Yanling Li; Lixia Du; Yue Hou

doi:10.1117/12.2678861

25 May 2023 YOLOv4-tiny pedestrian detection method based on attention fusion mechanism

Yanling Li, Lixia Du, Yue Hou

Proceedings Volume 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023); 127120R (2023) https://doi.org/10.1117/12.2678861
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 2023, Huzhou, China

Abstract

In order to solve the problem that the detection accuracy of YOLOv4-tiny algorithm decreases due to the lightweight and small weight of the model, a YOLOv4-tiny technology method based on attention fusion mechanism is proposed to detect pedestrians. In this method, CBAM is added to the effective feature layer and upper sampling layer extracted by FPN feature pyramid network based on the original YOLOv4-tiny algorithm to screen advanced semantic features. The 26×26 detection layer of the prediction feature layer was fused with the second layer of CSP structure with CBAM, and a 52×52 detection head was added to expand the coverage of pedestrian detection. Use IOU_NMS non-maximum suppression to remove some redundant candidate boxes and speed up the regression of prediction boxes. The Python script was used to extract the Person-class data image from PASCAL VOC2012 data set. The experimental results show that this method has improved the accuracy, recall rate and accuracy in the data verification set, achieving the purpose of accurately detecting pedestrians.

Citation Download Citation

Yanling Li, Lixia Du, and Yue Hou "YOLOv4-tiny pedestrian detection method based on attention fusion mechanism", Proc. SPIE 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 127120R (25 May 2023); https://doi.org/10.1117/12.2678861

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KEYWORDS

Detection and tracking algorithms

Target detection

Feature extraction

Education and training

Convolution

Data modeling

Deep learning

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