Abstract
Object detection in remote sensing images is full of challenges. The large scale of detection region, and the difficulty of dense object detection are the main problems needed to solve. Although YOLOv3 perform better and faster, it is not suitable to the orientation which means it is not friendly to NMS operation on dense objects area. This drawback would lead to an increase in miss rate in dense object detection. In this paper, we modified YOLOv3 based on the oriented bounding box (OBB) for object detection in remote images to solve the problems above. This model is based on YOLOv3 which performs better on small target compared with YOLOv2. We modified the architecture of YOLOv3 to predict oriented bounding box. In this way, we can obtain bounding boxes more suitable for large aspect ratio objects. Moreover, in a dense area such as parking lot, it can also achieve a good performance.
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Lei, J., Gao, C., Hu, J., Gao, C., Sang, N. (2019). Orientation Adaptive YOLOv3 for Object Detection in Remote Sensing Images. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11857. Springer, Cham. https://doi.org/10.1007/978-3-030-31654-9_50
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DOI: https://doi.org/10.1007/978-3-030-31654-9_50
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