Research on Pedestrian Detection Algorithm in Industrial Scene Based on Improved YOLOv7-Tiny

Abstract

YOLOv7 is one of the most effective algorithms for one-stage detectors. However, when it is applied to pedestrian detection tasks in the industrial scene, it is still challenging for complex environments and multi-scale changes of pedestrians. This paper proposes a new pedestrian detector for the industrial scene based on improved YOLOv7-tiny and named as GP-YOLO. First, the neck of YOLOv7-tiny is replaced by RepGFPN structure, make full use of multi-scale features to enhance the detection accuracy of objects with large-scale changes. Second, a new gⁿconv branch is added to the feature fusion module, and the high-order spatial interaction capability is introduced to further enhance the target detection accuracy. Finally, a lightweight method based on PModule is proposed, on this basis, a PConv bottleneck is designed to reduce the FLOPs and enhance the feature extraction. Experiments on a self-made Industrial Pedestrian Data set show that before lightweight, the proposed algorithm achieves a 3.2% improvement in [email protected]:0.95 and a 3.7% improvement in Recall compared to the baseline YOLOv7-tiny. After lightweight GP-YOLO, compared to non-lightweight, parameters and FLOPs are decreased by 26% and 23%, respectively, the [email protected]:0.95 is decreased by only 1.1% and the Recall is decreased by only 1.3%, which remains at a high level. Compared with baseline YOLOv7-tiny, the lightweight GP-YOLO has similar parameters and FLOPs, but the [email protected]:0.95 is increased by 2.1%, and the Recall is increased by 2.4%. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.