An improved Yolov5 real-time detection method for small objects captured by UAV

The object detection algorithm is mainly focused on detection in general scenarios, when the same algorithm is applied to drone-captured scenes, and the detection performance of the algorithm will be significantly reduced. Our research found that small objects are the main reason for this phenomenon. In order to verify this finding, we choose the yolov5 model and propose four methods to improve the detection precision of small object based on it. At the same time, considering that the model needs to be small in size, speed fast, low cost and easy to deploy in actual application, therefore, when designing these four methods, we also fully consider the impact of these methods on the detection speed. The model integrating all the improved methods not only greatly improves the detection precision, but also effectively reduces the loss of detection speed. Finally, based on VisDrone-2020, the mAP of our model is increased from 12.7 to 37.66%, and the detection speed is up to 55FPS. It is to outperform the earlier state of the art in detection speed and promote the progress of object detection algorithms on drone platforms.