Remote Sensing Image Object Detection Based on Improved YOLOv3 in Deep Learning Environment.

A deep learning-based method, improved YOLOv3 algorithm is proposed in the deep learning environment to tackle challenges such as big scale, uneven distribution, largescale variation, and complicated background of small- and medium-sized remote sensing photos. This manufacture uses Densenet as the backbone, replacing Darknet-53 to realize feature reuse and make the feature extraction more effective; introduces the spatial pyramid pooling module into the feature pyramid part for increasing the receptive field and isolating the most prominent contextual features; adds SE attention module in the process of feature extraction and obtains richer features by learning more location information and channel information from the images. Under DOTA dataset, the final results are that the mean Average Precision value is 86.78%, which is 4.16% higher than the baseline YOLOv3 network. The model put forward makes it easier to extract information from the feature map and achieve higher detection accuracy without influencing the real-time performance of detection. [ABSTRACT FROM AUTHOR]