YOLOv3 Detection Algorithm Based on the Improved Bounding Box Regression Loss

doi:10.19678/j.issn.1000-3428.0060479

Abstract

Abstract: The bounding box regression loss function in the YOLOv3 detection algorithm is sensitive to the bounding box scale but does not have a strong correlation with the optimization of the algorithm detection effect evaluation standard Intersection over Union(IoU).Furthermore, and the loss function cannot accurately reflect the overlap between the ground truth and prediction boxes, resulting in poor convergence effect.In response to these problems, in this study, an improved bounding box regression loss algorithm based on IoU is proposed, namely, BR-IoU.This algorithm adopts IoU as the loss term of the bounding box regression loss function, so that the boundary boxes with different scales can obtain more balanced loss optimization weight in the regression process.On this basis, it adds a penalty item to minimize the area of the rectangle enclosed by the center point of the ground truth and prediction boxes.The added penalty item improves the consistency of the aspect ratio between the ground truth and prediction boxes, which improves regression convergence effect of the bounding box.The experimental results on PASCAL VOC and COCO datasets demonstrate that YOLOv3 using BR-IoU can effectively improve the detection accuracy without affecting the real-time performance.Compared to YOLOV3 and G-YOLO algorithms, mAP value of the proposed algorithm increases by 2.5 and 1.5 percentage points respectively on PASCAL VOC test set and 2.07 and 0.66 percentage points respectively on COCO test set.

Key words: YOLOv3 detection algorithm, bounding box regression, Intersection over Union(IoU), BR-IoU loss algorithm, aspect ratio

摘要： YOLOv3检测算法中的边界框回归损失函数对边界框尺度敏感，且与算法检测效果评价标准交并比（IoU）之间的优化不具有强相关性，无法准确反映真值框与预测框之间的重叠情况，造成收敛效果不佳。针对上述问题，提出基于IoU的改进边界框回归损失算法BR-IoU。将IoU作为边界框回归损失函数的损失项，使不同尺度的边界框在回归过程中获得更均衡的损失优化权重。在此基础上，通过添加惩罚项最小化预测框与真值框中心点间围成的矩形面积，并提高预测框与真值框之间宽高比的一致性，从而优化边界框的回归收敛效果。在PASCAL VOC和COCO数据集上的实验结果表明，在不影响实时性的前提下，BR-IoU能够有效提高检测精度，采用BR-IoU的YOLOv3算法在PASCAL VOC 2007测试集上mAP较原YOLOv3算法和G-YOLO算法分别提高2.5和1.51个百分点，在COCO测试集上分别提高2.07和0.66个百分点。

关键词: YOLOv3检测算法, 边界框回归, 交并比, BR-IoU损失算法, 宽高比

CLC Number:

TP18

SHEN Jiquan, CHEN Xiangjun, ZHAI Haixia. YOLOv3 Detection Algorithm Based on the Improved Bounding Box Regression Loss[J]. Computer Engineering, 2022, 48(3): 236-243.

沈记全, 陈相均, 翟海霞. 基于改进边界框回归损失的YOLOv3检测算法[J]. 计算机工程, 2022, 48(3): 236-243.

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