MCISP-YOLOv5: YOLOv5-based multiscale channel interactive spatially perceptive small object detection algorithm

Jiangtao Guo; Kai Wang; Shu Cao; Wenzhong Yang

doi:10.1117/12.3042714

10 September 2024 MCISP-YOLOv5: YOLOv5-based multiscale channel interactive spatially perceptive small object detection algorithm

Jiangtao Guo, Kai Wang, Shu Cao, Wenzhong Yang

Author Affiliations +

Proceedings Volume 13257, International Conference on Advanced Image Processing Technology (AIPT 2024); 132570N (2024) https://doi.org/10.1117/12.3042714
Event: International Conference on Advanced Image Processing Technology (AIPT 2024), 2024, Chongqing, China

Abstract

Two central problems often faced when shooting at high altitudes from UAVs are many small and dense targets and complex background noise interference. In YOLOv5, due to multiple downsampling, the feature representation of small targets becomes weaker and may even be masked in the background. The Feature Pyramid Network (FPN) diminishes the detection accuracy of small targets due to its basic feature concatenation method, which underutilizes multiscale information and introduces extraneous contextual details. To solve the above problems, we propose a simple and effective improved model called multiscale channel interactive spatial perception yolov5 (MCISP-YOLOv5). First, we design a multiscale channel interaction spatial perception(MCISP) module, which recalibrates the channel features in each scale by interacting with information from different scales, facilitates the information flow between the shallow feature geometric information, and the more profound feature semantic information, and uses adaptive spatial learning to realize spatial perception so that the model can focus on the foreground objects better. Second, we replace the traditional up-sampling module with the Content-Aware ReAssembly of Features (CARAFE) operator for feature extraction, which enhances the feature characterization ability after multiple down-sampling, better recovers the detailed information. Finally, we added an additional, shallower depth feature map as the detection layer in YOLOv5. The supplementary feature map enhances the detection efficacy for small objects without adverse effects on the detection capabilities for other sizes of targets. Extensive experiments on the publicly available VisDrone2019 dataset show that the introduced model exhibits substantial enhancements in performance.

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

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Jiangtao Guo, Kai Wang, Shu Cao, and Wenzhong Yang "MCISP-YOLOv5: YOLOv5-based multiscale channel interactive spatially perceptive small object detection algorithm", Proc. SPIE 13257, International Conference on Advanced Image Processing Technology (AIPT 2024), 132570N (10 September 2024); https://doi.org/10.1117/12.3042714

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KEYWORDS

Object detection

Target detection

Convolution

Detection and tracking algorithms

Unmanned aerial vehicles

Small targets

Deformation

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