Abstract:

To reduce the influences of background interference factors in natural environments such as clouds, mist, dust, lights, sunrise, and sunset on the smoke and flame target detection accuracy, a smoke and fire detection algorithm based on an improved YOLO-V5 algorithm was proposed. Smoke, flame target images, and interference image data sets were obtained from the on-site collection and web crawling approaches to solve sample imbalance and improve model generalization ability. A bidirectional feature pyramid network (BiFPN) was used to replace the original feature pyramid network (FPN) + path aggregation network (PAN) structure, and then multi-scale feature fusion on the target was performed to strengthen the model feature fusion ability. At the same time, distance intersection-over-union(DIoU) non-maximum suppression(NMS) is used to replace the original non-maximum suppression (NMS) to speed up the convergence of the detection box loss function and enhance the model reasoning ability. The results showed that the improved algorithm's accuracy, recall rate, mean average precision(mAP) and FPR were 79.2%, 68.6%, 74.2%, and 12.8%, respectively. Compared with the original YOLO-V5 algorithm, the proposed algorithm improved accuracy rate, recall rate, and mAP by 1.9%, 0.9%, and 2.7%, respectively. Furthermore, the FPR was decreased by 3.7%.

Key words: YOLO-V5 algorithm, smoke, fire, target detection, false positive rate(FPR)