YOlOv5s-ACE: Forest Fire Object Detection Algorithm Based on Improved YOLOv5s

Abstract

To address the challenges of low detection accuracy, slow detection speed, coarse feature extraction, and the difficulty of detection deployment in complex forest fire backgrounds, this paper presents a forest fire object detection algorithm based on an improved YOLOv5s (YOLOv5s-ACE). The algorithm not only realizes the accurate identification of small objects, but also guarantees the accuracy and speed of detection. Firstly, YOLOv5s-ACE uses Copy-Pasting data enhancement to expand the small object sample set to reduce the overfitting risk in the process of model training. Secondly, it choose Atrous Spatial Pyramid Pooling (ASPP) to replace Spatial Pyramid Pooling (SPP) module in backbone part of YOLOv5 network. Therefore, the proposed algorithm can enlarge the receptive field while ensuring the resolution, which is conducive to the accurate positioning of small object forest flame. Third, after adding the Convolutional Block Attention Module (CBAM) module to the C3 module of the Neck layer, the key features of the forest flame object can be further screened, while irrelevant information that interferes with the flame detection, such as background information, can be eliminated. The network performance of forest fire detection is improved without increasing the depth, width and resolution of the input image. Finally, we replace CIOU losses (Complete-IoU) with EIOU losses (Efficient-IoU) to optimize the performance of the model and improve accuracy. The experimental results show that compared with the original algorithm, the proposed object detection algorithm improves mean Average Precision (mAP) by 5.6%, Precision by 2.7%, Recall by 6.5% and GFlops by 6.7%. Even compared with the YOLOv7 algorithm, the proposed algorithm YOLOv5s-ACE increases mAP by 0.9%, Precision by 2.2%, and Recall by 0.3%.