Fast and accurate multi-class geospatial object detection with large-size remote sensing imagery using CNN and Truncated NMS

Abstract

Multi-class geospatial object detection with remote sensing imagery has broad prospects in urban planning, natural disaster warning, industrial production, military surveillance and other applications. Accuracy and efficiency are two common measures for evaluating object detection models, and it is often difficult to achieve both at the same time. Developing a practical remote sensing object detection algorithm that balances the accuracy and efficiency is thus a big challenge in the Earth observation community. Here, we propose a comprehensive high-speed multi-class remote sensing object detection method. Firstly, we obtain a multi-volume YOLO (You Only Look Once) v4 model for balancing speed and accuracy, based on a pruning strategy of the convolutional neural network (CNN) and the one-stage object detection network YOLO v4. Moreover, we apply the Manhattan-Distance Intersection of Union (MIOU) loss function to the multi-volume YOLO v4 to further improve the accuracy without additional computational burden.Secondly, mainly due to computing limitations, a remote sensing image that is large-size relative to a natural image must first be divided into multiple smaller tiles, which are then detected separately, and finally, the detection results are spliced back to match the original image. In the process of remote sensing image slicing, a large number of truncated objects appear at the edge of tiles, which will produce a large number of false results in the subsequent detection links. To solve this problem, we propose a Truncated Non-Maximum Suppression (NMS) algorithm to filter out repeated and false detection boxes from truncated targets in the spliced detection results. We compare the proposed algorithm with the state-of-the-art methods on the Dataset for Object deTection in Aerial images (DOTA) and DOTA v2. Quantitative evaluations show that mAP and FPS reach 77.3 and 35 on DOTA, and 61.0 and 74 on DOTA v2. Overall, our method reaches the optimal balance between efficiency and accuracy, and realizes the high-speed remote sensing object detection.