Improvement of Low-Contrast Objective Detecting Capability for YOLOv5 Based on Receptive Field Enhancement and Redundant Feature Reuse-Reference-Cited by-同舟云学术

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Improvement of Low-Contrast Objective Detecting Capability for YOLOv5 Based on Receptive Field Enhancement and Redundant Feature Reuse

Published:2024-06-30 Issue: Volume:28 Page:1-9
ISSN:
Container-title:2024 International Joint Conference on Neural Networks (IJCNN)
language:
Short-container-title:

Author:

Xu Qingyu¹,Yu Jiguo²,Dong Anming¹

Affiliation:

1. Qilu University of Technology (Shandong Academy of Sciences),Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan),Jinan,China

2. Qilu University of Technology (Shandong Academy of Sciences),Big Data Institute,Jinan,China

Funder

National Natural Science Foundation of China

Qilu University of Technology

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10649807/10649898/10650738.pdf?arnumber=10650738

Reference34 articles.

1. TAF2-Net: Triple-Branch Attentive Feature Fusion Network for Ultrasonic Flaw Detection

2. EDRNet: Encoder–Decoder Residual Network for Salient Object Detection of Strip Steel Surface Defects

3. Surface Defect Detection of Steel Strips Based on Anchor-free Network with Channel Attention and Bidirectional Feature Fusion

4. ES-Net: Efficient Scale-Aware Network for Tiny Defect Detection

5. DefectNet: Toward Fast and Effective Defect Detection

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