国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

Hillside areas are prone to falling rocks, so it is difficult to detect the occurrence of disasters in time by manpower. In order to timely detect the occurrence of falling rocks and take countermeasures, a method of falling rocks detection based on improved YOLOX is proposed to automatically detect and report the occurrence of falling rocks. The self-made rockfall data set is used to train YOLOX network, optimize the spatial pyramid pool structure, and obtain more semantic information. The attention mechanism of ECA-Net(Efficient Channel Attention Module) channel is introduced to improve the feature extraction ability and information transmission between features. Meanwhile, the loss function is improved and data enhancement is used to improve the network training effect. The experimental results show that mAP@0.5 of the improved YOLOX algorithm is 92.50%, and the number of frames detected per second is 62.6. Compared with the YOLOX algorithm, mAP@0.5 is 3.45% higher and the number of frames detected per second is 0.3 higher. Compared with the original algorithm, the accuracy is improved greatly without loss of performance, and the real-time detection requirements of image and video data are met.