A Smoke Detection Algorithm Based on Improved YOLO v7 Lightweight Model for UAV Optical Sensors

As a crucial technology for improving the autonomous sensing capability of optical sensors on unmanned aerial vehicles (UAVs), object detection has emerged as a research focus in the UAV patrol inspection. To address the problem of high computational complexity in detection models carried by UAV, a lightweight network model based on YOLO v7 is designed. Based on this model, a smoke detection algorithm is developed. Firstly, a new multi-parameter gated activation function (Better-mish) based on Mish function is constructed. Secondly, CBS and CA modules are improved based on the Better-mish. The Improved-CA is replaced with the Conv in MPConv to build CA-MPConv. The improved modules are added into backbone of the model. Thirdly, the backbone is reconstructed in combination with RepVGG module. An improved YOLOv7 lightweight model is proposed and a smoke detection algorithm based on this model is developed. Finally, several experiments are conducted on Smoke and COCO datasets. Experimental results show three key points as follows. 1) Compared with SiLU, Mish and other activation functions, Better-mish can alleviate the gradient disappearance of neural networks in the saturation region. 2) Compared with YOLO v7, the improved YOLO v7 model reduces computational complexity by 93.24 % and only increases model parameters by less than 2 %. 3) The algorithm can maintain effective recognition of smoke images at low computational complexity. The smoke detection algorithm provides a theoretical basis and an effective algorithm for the early warning of fire hazards in the UAV patrol inspection.