Year: 2025
Type: article
Abstract: In order to achieve the automated qualitative evaluation of Ephestia kuehniella eggs in mass industrialization and production, YOLOv10-CR is presented, with a modified circular representation based on YOLOv10. While existing methods like CircleNet struggle with overlapping objects, YOLOv10, though highly effective in general object detection, is not optimized for spherical objects. To address these limitations, we integrate YOLOv10’s efficient architecture with CircleNet’s circular representation, introducing a modified circle representation (xyy format) and an improved circle intersection over union (cIOU) algorithm. The proposed xyy circle representation reduces degrees of freedom by encoding circles through top and bottom boundary points, effectively disentangling overlapping eggs caused by adhesive mucus. The improved cIOU algorithm avoids singularity issues in nested or tangent circles, enhancing robustness under rotational variations. Experimental results demonstrate that the proposed YOLOv10-CR achieves better detection accuracy, computational efficiency, and rotation robustness with lower computational costs for the detection of Ephestia kuehniella eggs.
Source: Applied Sciences
DOI: 10.3390/app152111501
Authors: Dongwei He, Chaohuang She, Yanxuan Zhang, Ying Ma, Lisang Liu, Jian Chen, Yi Chen
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $2,896
Award: G3848382867
OpenAlex ID: W4415615163