Smoking behavior detection algorithm based on YOLOv8-MNC - Wang, Zhong; Lei, Lanfang; Shi, Peibei (Frontiers in Computational Neuroscience, 2023)
Introduction: The detection of smoking behavior is an emerging field faced with challenges in identifying small, frequently occluded objects like cigarette butts using existing deep learning technologies. Such challenges have led to unsatisfactory detection accuracy and poor model robustness.Methods: To overcome these issues, this paper introduces a novel smoking detection algorithm, YOLOv8-MNC, which builds on the YOLOv8 network and includes a specialized layer for small target detection. The YOLOv8-MNC algorithm employs three key strategies: (1) It utilizes NWD Loss to mitigate the effects of minor deviations in object positions on IoU, thereby enhancing training accuracy;(2) It incorporates the Multi-head Self-Attention Mechanism (MHSA) to bolster the network's global feature learning capacity; (3) It implements the lightweight general up-sampling operator CARAFE, in place of conventional nearest-neighbor interpolation up-sampling modules, minimizing feature information loss during the up-sampling process.Results: Experimental results from a customized smoking behavior dataset demonstrate significant improvement in detection accuracy. The YOLOv8-MNC model achieved a detection accuracy of 85.887%, signifying a remarkable increase of 5.7% in the mean Average Precision (