Vehicle target detection from optical satellite image based on improved YOLOX algorithm
In order to improve the problems of low accuracy and speed of vehicle object detection in optical satellite images,a vehicle target detection method based on improved YOLOX algorithm is proposed. Firstly,taking the S version of the YOLOX model as the baseline,CSPDarknet-53 is used to replace the original backbone feature extraction network,and the convolutional block attention module(CBAM)is introduced to improve the attention to the vehicle target during feature extraction. Then,the output scale of backbone feature extraction network is expanded and a bidirectional feature pyramid network (BFPnet )is designed in feature enhancement extraction part. Sub-pixel convolutional upsampling method,horizontal jump connection and vertical cross-scale connection are used to realize the reuse of different level and scale features,so that the final output feature layer fully integrates the classification and positioning information. The experimental results show that the detection accuracy of the proposed algorithm for large-vehicle and small-vehicle is 88. 98％ and 86. 58％,respectively. Compared with the original algorithm,the average detection accuracy is increased by 5. 36％,and the detection speed reaches 58. 37 fps,which has a better detection effect.
万方数据
维普
