基于深度学习的森林火灾烟雾监测

Deep Learning-Based Forest Fire Smoke Detection

为了第一时间发现森林火灾,避免因森林火灾造成严重后果,提出以森林火灾烟雾为主要目标的检测模型YOLO-SCW,在YOLOv7的基础上,引入SPD-Conv层,以减小特征提取过程中小目标特征缺失的问题。之后在检测头金字塔池化部分增加坐标准意力机制模块,通过将位置信息编码到通道中,增加了模型对目标的关注度,并且减少了背景对检测效果的干扰。最后,通过WIoU矩形框损失函数,提高了预测框的回归速度与精度。在测试过程中,改进后的YOLO-SCW较YOLOv7模型mAP提高了9.1%,并减少了误检与漏检现象,证明YOLO-SCW有着更好的特征提取能力与泛化能力,对森林火灾烟雾监测任务表现出色。

In order to detect forest fires in the first time and avoid serious consequences caused by forest fires,a detection model YOLO-SCW with forest fire smoke as the main target is proposed,and the SPD-Conv layer is introduced based on YOLOv7 to reduce the problem of missing features of small targets in the feature extraction process.Then,the Coordinate Pay module is added in the pooling part of the detection head pyramid,and the location information is encoded into the channel,which increases the attention of the modelto the target and reduces the interference of the background on the detection effect.Finally,the WIoU rectangular box loss function is used to improve the regression speed and accuracy of the prediction box.During the test,the improved YOLO-SCW increased by 9.1%compared with the mAP of the YOLOv7 model,and reduced the false detection and missed detection,which proved that YOLO-SCW has better feature extraction and generalization ability,and has excellent performance for forest fire smoke detection tasks.