SFGNet detecting objects via spatial fine-grained feature and enhanced RPN with spatial context
Author:
Affiliation:
1. School of Computer Science and Engineering, Northeastern University, Liaoning, People's Republic of China
2. Neusoft Reachauto Corporation, Liaoning, People's Republic of China
Funder
Research and development of L3 commercial vehicle autonomous driving system based on artificial intelligence
Research and development and industrialization of key technologies for intelligent driving vehicles
Publisher
Informa UK Limited
Subject
Artificial Intelligence,Control and Optimization,Control and Systems Engineering
Link
https://www.tandfonline.com/doi/pdf/10.1080/21642583.2022.2062479
Reference52 articles.
1. Object Detection Using Strongly-Supervised Deformable Part Models
2. Bell, S., Zitnick, C. L., Bala, K. & Girshick, R. B. (2015). Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks. CoRR, abs/1512.04143.
3. Bochkovskiy, A., Wang, C. & Liao, H. M. (2020). YOLOv4: Optimal speed and accuracy of object detection. CoRR, abs/2004.10934.
4. Soft-NMS — Improving Object Detection with One Line of Code
5. Prime Sample Attention in Object Detection
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