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基于改进YOLOv7与DeepSORT相结合的咸蛋黄杂质在线检测方法

On-Line Detection Method of Salted Egg Yolks with Impurities Based on Improved YOLOv7 Combined with DeepSORT.

作者信息

Gong Dongjun, Zhao Shida, Wang Shucai, Li Yuehui, Ye Yong, Huo Lianfei, Bai Zongchun

机构信息

College of Engineering, Huazhong Agricultural University, Wuhan 430070, China.

Wuhan Vocational College of Software and Engineering (Wuhan Open University), Wuhan 430205, China.

出版信息

Foods. 2024 Aug 16;13(16):2562. doi: 10.3390/foods13162562.

Abstract

Salted duck egg yolk, a key ingredient in various specialty foods in China, frequently contains broken eggshell fragments embedded in the yolk due to high-speed shell-breaking processes, which pose significant food safety risks. This paper presents an online detection method, YOLOv7-SEY-DeepSORT (salted egg yolk, SEY), designed to integrate an enhanced YOLOv7 with DeepSORT for real-time and accurate identification of salted egg yolks with impurities on production lines. The proposed method utilizes YOLOv7 as the core network, incorporating multiple Coordinate Attention (CA) modules in its Neck section to enhance the extraction of subtle eggshell impurities. To address the impact of imbalanced sample proportions on detection accuracy, the Focal-EIoU loss function is employed, adaptively adjusting bounding box loss values to ensure precise localization of yolks with impurities in images. The backbone network is replaced with the lightweight MobileOne neural network to reduce model parameters and improve real-time detection performance. DeepSORT is used for matching and tracking yolk targets across frames, accommodating rotational variations. Experimental results demonstrate that YOLOv7-SEY-DeepSORT achieves a mean average precision (mAP) of 0.931, reflecting a 0.53% improvement over the original YOLOv7. The method also shows enhanced tracking performance, with Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP) scores of 87.9% and 73.8%, respectively, representing increases of 17.0% and 9.8% over SORT and 2.9% and 4.7% over Tracktor. Overall, the proposed method balances high detection accuracy with real-time performance, surpassing other mainstream object detection methods in comprehensive performance. Thus, it provides a robust solution for the rapid and accurate detection of defective salted egg yolks and offers a technical foundation and reference for future research on the automated and safe processing of egg products.

摘要

咸鸭蛋蛋黄是中国各种特色食品中的关键成分,由于高速破壳过程,蛋黄中经常含有嵌入的碎蛋壳碎片,这带来了重大的食品安全风险。本文提出了一种在线检测方法,即YOLOv7-SEY-DeepSORT(咸鸭蛋蛋黄,SEY),旨在将增强的YOLOv7与DeepSORT集成,以实时、准确地识别生产线上带有杂质的咸鸭蛋蛋黄。该方法以YOLOv7作为核心网络,在其颈部部分融入多个坐标注意力(CA)模块,以增强对细微蛋壳杂质的提取。为了解决样本比例不平衡对检测精度的影响,采用了Focal-EIoU损失函数,自适应调整边界框损失值,以确保图像中带有杂质的蛋黄的精确定位。骨干网络被轻量级的MobileOne神经网络取代,以减少模型参数并提高实时检测性能。DeepSORT用于跨帧匹配和跟踪蛋黄目标,以适应旋转变化。实验结果表明,YOLOv7-SEY-DeepSORT的平均精度均值(mAP)达到0.931,比原始的YOLOv7提高了0.53%。该方法还显示出增强的跟踪性能,多目标跟踪准确率(MOTA)和多目标跟踪精度(MOTP)分数分别为87.9%和73.8%,比SORT分别提高了17.0%和9.8%,比Tracktor分别提高了2.9%和4.7%。总体而言,该方法在高检测精度和实时性能之间取得了平衡,在综合性能上超过了其他主流目标检测方法。因此,它为快速、准确地检测有缺陷的咸鸭蛋蛋黄提供了一个强大的解决方案,并为未来蛋类产品的自动化和安全加工研究提供了技术基础和参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44e3/11353706/057774057137/foods-13-02562-g001.jpg

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