With the continuous increase in the number of domestic optical satellites,the obtained satellite image data has shown a large-scale increase. There is a considerable proportion of image radiation anomalies in the images obtained by satellites and processed through sensor correction. Image radiation quality is an important factor determining the evaluation of image quality inspection level. Currently,its inspection mainly adopts human-computer interaction. In response to the current radiation problem in optical image quality inspection,an improved YOLOv5 deep learning network is proposed to identify targets in radiation abnormal areas. Integrate the improved light BiFPN feature fusion network and ShuffleNetV2 backbone network into YOLOv5s. By exploring the principle of image radiation anomalies,this network can accurately determine the range of targets in radiation anomaly images. The trained model can effectively detect the range of radiation issues through anchor frames,lay the foundation for further model deployment and application.
deep learninglight weightremote sensing imagesradiation anomalyobject detection
NETL
NSTL
万方数据
