Improved real time object detection method for remote sensing image based on YOLOv4

The rapid development of computer vision has brought great convenience to remote sensing image processing technology, significantly improved the accuracy of target detection in remote sensing images and saved human effort. However, in the real-time remote sensing detection work, the complex and changeable weather conditions bring great difficulties to the detection. The existing models have shortcomings such as missed detection, slow detection speed, bloated and complex, and large number of parameters. In this paper, we propose a lightweight rapid deployment network framework based on ghostnet improved yolov4. First, ghostnet real-time lightweight extraction is introduced to speed up the search speed and save training costs; secondly, improve the Panet fusion network and add a self-attention mechanism at the up sampling point; use the K++ algorithm to optimize the size of the anchor of the remote sensing image detection target, and finally improve the loss function and add hyperparameters to suppress the imbalance between the target and background categories during training. Experiments show that compared with the original version of YOLOV4mAP in the data set, the mAP is increased by 2.2%, the calculation amount is reduced by 35%, and the FPS is increased by 78%. Therefore, G-YOLOv4 does have better performance in mAP, model size and FPS compared to YOLOV4's mAP, model size and FPS.