DSNet: Joint Semantic Learning for Object Detection in Inclement Weather Conditions.

IEEE Transactions on Pattern Analysis and Machine Intelligence
Shih-Chia HuangDa-Wei Jaw

Abstract

In the past half of the decade, object detection approaches based on the convolutional neural network have been widely studied and successfully applied in many computer vision applications. However, detecting objects in inclement weather conditions remains a major challenge because of poor visibility. In this article, we address the object detection problem in the presence of fog by introducing a novel dual-subnet network (DSNet) that can be trained end-to-end and jointly learn three tasks: visibility enhancement, object classification, and object localization. DSNet attains complete performance improvement by including two subnetworks: detection subnet and restoration subnet. We employ RetinaNet as a backbone network (also called detection subnet), which is responsible for learning to classify and locate objects. The restoration subnet is designed by sharing feature extraction layers with the detection subnet and adopting a feature recovery (FR) module for visibility enhancement. Experimental results show that our DSNet achieved 50.84 percent mean average precision (mAP) on a synthetic foggy dataset that we composed and 41.91 percent mAP on a public natural foggy dataset (Foggy Driving dataset), outperforming many state-of-the...Continue Reading

Citations

May 1, 2021·Sensors·Dat NgoBongsoon Kang
Aug 29, 2021·Sensors·Shih-Chia HuangSha-Wo Huang

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Software Mentioned

MultiNet
MSCNN
Faster R - CNN
CNN
DSNet
YOLO
RetinaNet
CAP
Faster
R

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