Flame Smoke Detection Algorithm Based on Improved YOLOX_S
In response to the current issues of poor flame and smoke detection performance and high false alarm rates in fire warning,an improved fire and smoke detection algorithm was proposed based on the YOLOX framework.In the data collection phase,the used dataset consists of 9 621 images,including data from the publicly available Bilkent University dataset and a portion of self-built data.The data diversity was enhanced through the adoption of Mosaic data augmentation.Subsequently,the original CSPDarkNet backbone was replaced with the swin-T backbone network,which better captured features at different scales,effectively improving the accuracy of object detection.Additionally,the BiFPN(bidirectional feature pyramid network)feature fusion network was introduced to the network model,enhancing detection efficiency and adaptability,thus maintaining high detection accuracy even in complex backgrounds.Finally,the CA attention mechanism was incorporated to strengthen the feature extraction capabilities of this algorithm.Comparative experiments show that the improved YOLOX_S fire and smoke detection algorithm achieves high accuracy,with mAP@0.5(the average detection accuracy when the threshold for the degree of overlap between the predicted box and the true box is 0.5)reaching 81.5％,representing a 5.3％improvement compared to the original network.The improved YOLOX_S network model has higher accuracy and lower false alarm rate for flame smoke detection.
万方数据
维普
