Smoking behavior recognition algorithm based on lightweight object detection network

Abstract

Smoking is a common behavior in daily life, but smoking in public places not only affects public health and the health of others, but also may cause accidents such as fires. Therefore, detecting and recognizing smoking behavior is of great importance. Aiming at the problem that most current target detection networks are large in volume, have many parameters, and are difficult to deploy on low-performance device terminals, the article proposes a lightweight improved YOLOv5 smoking behavior detection algorithm model. This model replaces the backbone with a lightweight MobileNetv3 neural network, and at the same time absorbs and references the design ideas of ShuffleNet to improve the width of the neck-head part, making it smaller and more suitable for deployment on low-configuration devices. The experimental data show that after improvement, the new network has 80% less parameters and 63% less inference time, and when deployed on low-configuration device (i3-4000-m 2.4-ghz 2c4t), its inference time (50-ms) can be reduced by half compared to the original network (120-ms) and meet the requirement of near real-time. Therefore, the improved network can reduce network parameters while ensuring accuracy and can achieve near real-time performance on low-configuration devices.