arrow
返回

Learning Offset Probability Distribution for Accurate Object Detection

delete2024-01-22
delete0
PRE
AI
H
Heqian Qiu
李宏亮 封面图
李宏亮 (Hongliang Li)
Q
Qingbo Wu
石恒璨 封面图
石恒璨 (Hengcan Shi)
L
Lanxiao Wang *
F
Fanman Meng
L
Linfeng Xu
DOI:10.1145/3637214delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
Object detection combines object classification and object localization problems. Current object detection methods heavily depend on regression networks to locate objects, which are optimized with various regression loss functions to predict offsets between candidate boxes and objects. However, these regression losses are difficult to assign the appropriate penalties for samples with large offset errors, resulting in suboptimal regression networks and inaccurate object offsets. In this article, we consider object location as offset bin classification problem, and propose a distance-aware offset bin classification network optimized with multiple binary cross entropy losses to learn various offset probability distribution, including single label distribution and distance-aware label distribution. On one hand, it provides gradient contributions for different samples based on the bounded probability instead of previous incalculable offset error. On the other hand, it explores the distance correlations between discrete offset bins to facilitate network learning. Specifically, we discretize the continuous offset into a number of bins, and predict the probability of each offset bin, in which the probability should be higher for the offset bin closer to the target offsets, and vice versa. Furthermore, we propose an expectation-based offset prediction and a hierarchical focusing method to improve the precision of prediction. We conduct extensive experiments to evaluate the effectiveness of our method. In addition, our method can be conveniently and flexibly inserted into existing object detection methods, which consistently achieves a large gain based on popular anchor-based and anchor-free methods on the PASCAL VOC, MS-COCO, KITTI, and CrowdHuman datasets. Code will be released at: https://github.com/QiuHeqian/DBC.
Keyword:
Object detection
distance-aware offset bin classification
offset probability distribution
expectation-based offset prediction
hierarchical focusing method

期刊

ACM Transactions on Multimedia Computing Communications and Applications 封面图
ACM Transactions on Multimedia Computing Communications and Applications
IF:
6
论文数:
2.0K
被引数:
5.4K

机构

暂无机构信息