Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation

Deep learning-based object detection and instance segmentation have achieved unprecedented progress. Taking YOLACT on MS COCO as an example, our method achieves performance gains as +1.7 AP and +6.2 AR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">100</sub> for object detection, and +1.1 AP and +3.5 AR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">100</sub> for instance segmentation, with 27.1 FPS o...

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• 1 In this article, we propose complete-IoU (CIoU) loss and Cluster-NMS for enhancing geometric factors in both bounding-box regression and nonmaximum suppression (NMS), leading to notable gains of average precision (AP) and average recall (AR), without the sacrifice of inference efficiency.
• 2 In particular, we consider three geometric factors, that is: 1) overlap area; 2) normalized central-point distance; and 3) aspect ratio, which are crucial for measuring bounding-box regression in object detection and instance segmentation.
• 3 The three geometric factors are then incorporated into CIoU loss for better distinguishing difficult regression cases.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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