子网
行人检测
计算机科学
人工智能
计算机视觉
特征(语言学)
特征学习
多任务学习
目标检测
任务(项目管理)
Boosting(机器学习)
深度学习
融合机制
行人
特征提取
图像融合
模式识别(心理学)
图像(数学)
融合
工程类
脂质双层融合
哲学
系统工程
语言学
计算机安全
运输工程
作者
Yuanzhi Wang,Tao Lü,Tao Zhang,Yuntao Wu
出处
期刊:Sensors
[MDPI AG]
日期:2020-10-16
卷期号:20 (20): 5852-5852
被引量:7
摘要
Pedestrian detection is an essential problem of computer vision, which has achieved tremendous success under controllable conditions using visible light imaging sensors in recent years. However, most of them do not consider low-light environments which are very common in real-world applications. In this paper, we propose a novel pedestrian detection algorithm using multi-task learning to address this challenge in low-light environments. Specifically, the proposed multi-task learning method is different from the most commonly used multi-task learning method-the parameter sharing mechanism-in deep learning. We design a novel multi-task learning method with feature-level fusion and a sharing mechanism. The proposed approach contains three parts: an image relighting subnetwork, a pedestrian detection subnetwork, and a feature-level multi-task fusion learning module. The image relighting subnetwork adjusts the low-light image quality for detection, the pedestrian detection subnetwork learns enhanced features for prediction, and the feature-level multi-task fusion learning module fuses and shares features among component networks for boosting image relighting and detection performance simultaneously. Experimental results show that the proposed approach consistently and significantly improves the performance of pedestrian detection on low-light images obtained by visible light imaging sensor.
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