CM-YOLO: small object detection network based on contextual feature enhancement and multi-level feature fusion

Caifeng Wang; Weiqian Li

doi:10.1117/12.3049769

8 November 2024 CM-YOLO: small object detection network based on contextual feature enhancement and multi-level feature fusion

Caifeng Wang, Weiqian Li

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134162K (2024) https://doi.org/10.1117/12.3049769
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

Small target detection is a crucial and difficult task in computer vision, which faces difficulties such as low resolution, low pixel share of objects, and the complexity of feature extraction. To alleviate the problems mentioned above, this paper proposes a small target detection network called CM-YOLO. It uses the one-stage detection algorithm YOLOv8s as the basic framework to make a series of improvements, which include: designing the contextual feature enhancement module(CFEM)that incorporates an efficient multiscale attention mechanism to enhance the capability to extract features of small targets; proposing the multi-level feature fusion structure (MFS) to improve the capability of multi-scale feature fusion through the bi-directional multiscale feature propagation and weighted fusion mechanism. To demonstrate the effectiveness of CM-YOLO, this paper conducts extensive experiments on VisDrone 2019 and PASCAL VOC datasets, and the experimental results show that CM-YOLO has obvious performance improvement in small target detection relative to most existing algorithms.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Caifeng Wang and Weiqian Li "CM-YOLO: small object detection network based on contextual feature enhancement and multi-level feature fusion", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134162K (8 November 2024); https://doi.org/10.1117/12.3049769

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