Cotton-YOLO: Improved YOLOV7 for rapid detection of foreign fibers in seed cotton

Li, Qingxu; Ma, Wenjing; Li, Hao; Zhang, Xuedong; Zhang, Ruoyu; Zhou, Wanhuai

doi:10.1016/j.compag.2024.108752

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…Rapid manual sorting makes it challenging to distinguish foreign fibers from cotton accurately, resulting in slow detection work and increased production time costs. In recent years, the rapid development of artificial intelligence technology, particularly deep learning, has led to significant advancements in foreign fiber detection methods [4][5][6][7][8]. Xuehua Zhao [4] used the feature selection method to match classifiers and select the optimal feature set for detecting foreign fibers, obtaining excellent performance in foreign fiber detection with Extreme Learning Machine and Kernel Support Vector Machine, which achieved classification accuracies of 93.61% and 93.17% respectively, using feature sets of 42 and 52 features.…”

Section: Introductionmentioning

confidence: 99%

“…Xuehua Zhao [4] used the feature selection method to match classifiers and select the optimal feature set for detecting foreign fibers, obtaining excellent performance in foreign fiber detection with Extreme Learning Machine and Kernel Support Vector Machine, which achieved classification accuracies of 93.61% and 93.17% respectively, using feature sets of 42 and 52 features. Qingxu Li [5] designed "Cotton-YOLO" for the efficient detection of foreign fibers in seed cotton, achieving an accuracy of 99.12%, an mAP50 of 96.92%, and a detection speed of 132.2 FPS (7.6 ms per image), significantly outperforming YOLOV7. Yuhong Du [6] improved Faster RCNN for the diversity of foreign fiber size and shape characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Study on the Detection Mechanism of Multi-Class Foreign Fiber under Semi-Supervised Learning

Zhou,

Wei,

Huang

et al. 2024

Applied Sciences

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Foreign fibers directly impact the quality of raw cotton, affecting the prices of textile products and the economic efficiency of cotton textile enterprises. The accurate differentiation and labeling of foreign fibers require domain-specific knowledge, and labeling scattered cotton foreign fibers in images consumes substantial time and labor costs. In this study, we propose a semi-supervised foreign fiber detection approach that uses unlabeled image information and a small amount of labeled data for model training. Our proposed method, Efficient YOLOv5-cotton, introduces CBAM to address the issue of the missed detection and false detection of small-sized cotton foreign fibers against complex backgrounds. Second, the algorithm designs a multiscale feature information extraction network, SPPFCSPC, which improves its ability to generalize to fibers of different shapes. Lastly, to reduce the increased network parameters and computational complexity introduced by the SPPFCSPC module, we replace the C3 layer with the C3Ghost module. We evaluate Efficient YOLOv5 for detecting various types of foreign fibers. The results demonstrate that the improved Efficient YOLOv5-cotton achieves a 1.6% increase in mAP@0.5 (mean average precision) compared with the original Efficient YOLOv5 and reduces model parameters by 10% compared to the original Efficient YOLOv5 with SPPFCSPC. Our experiments show that our proposed method enhances the accuracy of foreign fiber detection using Efficient YOLOv5-cotton and considers the trade-off between the model size and computational cost.

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