Modified YOLOv5 for small target detection in aerial images

Object detection is an important field in computer vision. Detecting objects in aerial images is an extremely challenging task as the objects can be very small compared to the size of the image, the objects can have any orientation, and depending upon the altitude, the same object can appear in different sizes. YOLOv5 is a recent object detection algorithm that has a good balance of accuracy and speed. This work focuses on enhancing the YOLOv5 object detection algorithm specifically for small target detection. The accuracy on small objects has been improved by adding a new feature fusion layer in the feature pyramid part of YOLOv5 and using compound scaling to increase the input size. The modified YOLOv5 demonstrates a remarkable 11% improvement in mAP 0.5 on the small vehicle class of the DOTA dataset while being 25% smaller in terms of GFLOPS and achieving a 10.52% faster inference time, making it well-suited for real-time applications. Furthermore, the modified YOLOv5 achieves a notable 45.2% mAP 0.5 compared to 31.7% mAP 0.5 of YOLOv5 on the challenging VisDrone dataset. The modified YOLOv5 outperforms many state-of-the-art algorithms in small target detection in aerial images. In addition to performance evaluation, we also present an analysis of object sizes in pixel areas in the VisDrone and DOTA datasets. The proposed modifications demonstrate the potential for significant advancements in small target detection in aerial images and provide valuable insights for further research in this area.