关键词: 火灾检测, YOLOV3-IMP, 多尺度检测, Softer-NMS

Abstract:

Aiming at the problems of traditional fire detectors with limited detection range, low accuracy of traditional fire detection algorithms and long detection time, a fire detection method YOLOV3-IMP based on improved YOLOV3 is proposed. YOLOV3 network structure is improved to enhance the ability to learn shallow features, including improvements to feature extraction networks and multi-scale detection. Initial prior frames are generated through an improved [K]-means clustering algorithm. Detection capabilities are improved for small fire areas through an improved loss function. Softer-NMS algorithm is adopted before outputting fire detection images to improve detection ability for overlapping areas. Experimental results show that the improved algorithm’s accuracy and recall rate is 91.6%, 83.2%, mAP is as high as 84.5%, and the detection speed is up to 0.28?s. It can meet the real-time and accuracy of fire detection, which can achieve effective fire detection.

Key words: fire detection, YOLOV3-IMP, multi-scale detection, Softer-NMS