PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments

• URL: http://arxiv.org/abs/2007.09584v1
• Date: Sun, 19 Jul 2020 03:51:59 GMT
• Title: PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments
• Authors: Zhiming Chen and Kean Chen and Weiyao Lin and John See and Hui Yu and Yan Ke and Cong Yang
• Abstract summary: An oriented bounding box (OBB) can better target rotated objects by reducing the overlap with background areas. Existing OBB approaches are mostly built on horizontal bounding box detectors by introducing an additional dimension optimized by a distance loss. A novel loss, Pixels-IoU (PIoU) Loss, is formulated to exploit both the angle and IoU for accurate OBB regression.
• Score: 33.27663718573774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object detection using an oriented bounding box (OBB) can better target rotated objects by reducing the overlap with background areas. Existing OBB approaches are mostly built on horizontal bounding box detectors by introducing an additional angle dimension optimized by a distance loss. However, as the distance loss only minimizes the angle error of the OBB and that it loosely correlates to the IoU, it is insensitive to objects with high aspect ratios. Therefore, a novel loss, Pixels-IoU (PIoU) Loss, is formulated to exploit both the angle and IoU for accurate OBB regression. The PIoU loss is derived from IoU metric with a pixel-wise form, which is simple and suitable for both horizontal and oriented bounding box. To demonstrate its effectiveness, we evaluate the PIoU loss on both anchor-based and anchor-free frameworks. The experimental results show that PIoU loss can dramatically improve the performance of OBB detectors, particularly on objects with high aspect ratios and complex backgrounds. Besides, previous evaluation datasets did not include scenarios where the objects have high aspect ratios, hence a new dataset, Retail50K, is introduced to encourage the community to adapt OBB detectors for more complex environments.

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