Article: A feature fusion pedestrian detection algorithm Journal: International Journal of Wireless and Mobile Computing (IJWMC) 2025 Vol.29 No.2 pp.131 - 141 Abstract: When pedestrians are in different angles and positions, the feature extraction and fusion capabilities of YOLO series models are often limited. Aimed at this problem, we propose an improved feature fusion pedestrian detection algorithm YOLO-SCr. To enhance the ability of cross-scale feature extraction and detection speed, we reconstruct the network structure of the YOLO algorithm in the backbone part and convolution layer part, respectively. Then, to strengthen the feature fusion ability of pedestrians at different scales, we introduce the spatial pyramid pooling module and shuffle and Convolutional Block Attention Module (CBAM) attention mechanisms in different positions before YOLO layers. The experimental results show that compared with the detection algorithm such as YOLOv3, YOLO-SCr can effectively improve the detection accuracy, recall and speed. Inderscience Publishers - linking academia, business and industry through research

Title: A feature fusion pedestrian detection algorithm

Authors: Nan Xiang; Lu Wang; Xiaoxia Ma; Chongliu Jia; Yuemou Jian; Lifang Zhu

Addresses: Liangjiang International College, Chongqing University of Technology, Chongqing, China; Chongqing Jialing Special Equipment Co. Ltd., Chongqing, China ' Liangjiang International College, Chongqing University of Technology, Chongqing, China; Chongqing Jialing Special Equipment Co. Ltd., Chongqing, China ' Liangjiang International College, Chongqing University of Technology, Chongqing, China ' Liangjiang International College, Chongqing University of Technology, Chongqing, China ' Liangjiang International College, Chongqing University of Technology, Chongqing, China ' Chongqing Jialing Special Equipment Co. Ltd., Chongqing, China

Abstract: When pedestrians are in different angles and positions, the feature extraction and fusion capabilities of YOLO series models are often limited. Aimed at this problem, we propose an improved feature fusion pedestrian detection algorithm YOLO-SCr. To enhance the ability of cross-scale feature extraction and detection speed, we reconstruct the network structure of the YOLO algorithm in the backbone part and convolution layer part, respectively. Then, to strengthen the feature fusion ability of pedestrians at different scales, we introduce the spatial pyramid pooling module and shuffle and Convolutional Block Attention Module (CBAM) attention mechanisms in different positions before YOLO layers. The experimental results show that compared with the detection algorithm such as YOLOv3, YOLO-SCr can effectively improve the detection accuracy, recall and speed.

Keywords: YOLO series; feature extraction; feature fusion; spatial pyramid pooling; pedestrian detection; shuffle and CBAM attention.

DOI: 10.1504/IJWMC.2025.148061

International Journal of Wireless and Mobile Computing, 2025 Vol.29 No.2, pp.131 - 141

Received: 17 Aug 2022
Received in revised form: 04 Nov 2022
Accepted: 17 Nov 2022
Published online: 25 Aug 2025 *

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Title: A feature fusion pedestrian detection algorithm

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