Version 1
: Received: 5 July 2024 / Approved: 5 July 2024 / Online: 5 July 2024 (12:01:05 CEST)
How to cite:
Hua, C.; Luo, K.; Wu, Y.; Shi, R. YOLO-ABD: A Multi-Scale Detection Model for Pedestrian Anomaly Behavior Detection. Preprints2024, 2024070522. https://doi.org/10.20944/preprints202407.0522.v1
Hua, C.; Luo, K.; Wu, Y.; Shi, R. YOLO-ABD: A Multi-Scale Detection Model for Pedestrian Anomaly Behavior Detection. Preprints 2024, 2024070522. https://doi.org/10.20944/preprints202407.0522.v1
Hua, C.; Luo, K.; Wu, Y.; Shi, R. YOLO-ABD: A Multi-Scale Detection Model for Pedestrian Anomaly Behavior Detection. Preprints2024, 2024070522. https://doi.org/10.20944/preprints202407.0522.v1
APA Style
Hua, C., Luo, K., Wu, Y., & Shi, R. (2024). YOLO-ABD: A Multi-Scale Detection Model for Pedestrian Anomaly Behavior Detection. Preprints. https://doi.org/10.20944/preprints202407.0522.v1
Chicago/Turabian Style
Hua, C., Yadong Wu and Rui Shi. 2024 "YOLO-ABD: A Multi-Scale Detection Model for Pedestrian Anomaly Behavior Detection" Preprints. https://doi.org/10.20944/preprints202407.0522.v1
Abstract
Public safety and intelligent surveillance systems critically depend on anomaly behavior detection for effective monitoring. In real-world pedestrian detection scenarios, prevalent challenges such as missed detections, complex background interference, and small target sizes hinder accurate anomaly identification. To address these issues, this study introduces YOLO-ABD, a lightweight method for anomaly behavior detection that integrates small target detection and channel shuffling. This method employs YOLOv8n as the baseline model, incorporating a small target detection mechanism in the Head part and utilizing GSConv convolution in the Backbone to enhance perceptual capability. Additionally, the SimAM attention mechanism is integrated to mitigate complex background interference, thereby improving target detection performance. Evaluation on the IITB-Corridor dataset demonstrated mAP50 and mAP50-95 scores of 89.3% and 60.6%, respectively. Generalization testing on the street-view-gdogo dataset further highlighted the superiority of YOLO-ABD over advanced detection algorithms, underscoring its effectiveness and generalizability. With a relatively small parameter count, YOLO-ABD presents an excellent lightweight solution for pedestrian anomaly behavior detection.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
