Research on Tunnel Fire Detection Based on Improved YOLOv8
There are the difficulties of detecting fires inside tunnels and directly deploying to the embedded devices with limited resources for real-time detection,a tunnel fire detection algorithm based on improved YOLOv8 is proposed.Firstly,the polarized at-tention mechanism is introduced to preserve high-resolution information and suppress redundant features,while enhancing the capture of global information.Secondly,the novel partial Convolution(PConv)is introduced to achieve the model with low latency and high throughput.Finally,the WIoU function is used to optimize the loss of network bounding box,enabling the fast convergence of the network.Experimental results demonstrate that on the utilized tunnel fire dataset,the mean average precision(mPA)of the proposed network improves by 1.3％.Furthermore,the model parameters of the lightweight model reduces by 29.7％,and the forward infer-ence time by 44％.The algorithm meets the requirements of accuracy and lightweight,making it suitable for real-time detection in tunnel scenarios.
万方数据
维普
