Algorithm for smoking detection based on upgraded YOLACT

Smoking in public areas poses significant risks to both individual health and the well-being of bystanders. Given the microscopic nature of smoke particles, detecting them can be challenging. To address the problems of its low detection accuracy, easy to be obscured and ignored, we propose a segmentation approach based on YOLACT_SMOKE. In this study, we enhance the original YOLACT model by integrating the smoother Mish activation function, which optimizes model speed without sacrificing performance. Additionally, we replace the Res2Net module with the ResNet module to improve feature extraction and expression. Furthermore, we introduce the CBAM attention mechanism to the C3 and C4 layers to mitigate noise interference. Recognizing the lack of suitable datasets, we curated a self-made smoking dataset for training and evaluation purposes. Experimental results demonstrate a notable 4.8% increase in accuracy at IoU thresholds of 0.5 and 0.75 compared to the pre-upgrade model, with respective improvements of 5.3% and 6.7% over the original model.