IA-Det: Iterative Attention-Based Robust Object Detection in Adverse Traffic Scenes-Reference-Cited by-同舟云学术

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IA-Det: Iterative Attention-Based Robust Object Detection in Adverse Traffic Scenes

Published:2024 Issue: Volume:73 Page:1-14
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Li Hao¹^ORCID,Yu Kailong²^ORCID,Qiu Junhui²^ORCID,Wang Zheng¹^ORCID,Yang Yang²^ORCID

Affiliation:

1. National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, China

2. School of Information Science and Technology, Yunnan Normal University, Kunming, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/19/10367905/10639492.pdf?arnumber=10639492

Reference56 articles.

1. A method of cross-layer fusion multi-object detection and recognition based on improved faster R-CNN model in complex traffic environment;Li;Pattern Recognit. Lett.,2021

2. Jointly Learning Deep Features, Deformable Parts, Occlusion and Classification for Pedestrian Detection

3. Pedestrian Recognition Using Cross-Modality Learning in Convolutional Neural Networks

4. SARNet: Spatial Attention Residual Network for pedestrian and vehicle detection in large scenes

5. Traffic-light sign recognition using capsule network

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