Mr. Yifei Chen Profile

Yifei Chen

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Proceedings Article | 29 January 2024 Paper

Research on swine trajectory tracking algorithm based on object detection

Dan Li, Shuo Xu, Feng Lu, Yifei Chen, Muhan Xue, Guohui Cui

Proceedings Volume 12984, 1298406 (2024) https://doi.org/10.1117/12.3017909

KEYWORDS: Detection and tracking algorithms, Object detection, Education and training, Motion detection, Convolution, 3D tracking

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The tracking algorithm for swine plays a pivotal role in efficiently extracting the movement trajectories and quantifying the motion patterns of pigs, thereby serving as an indicator of their physical well-being. Consequently, the task of swine tracking assumes paramount significance. Addressing the issues of low automation, significant error rates, and poor real-time performance in pig tracking, this study introduced a deep learning-based algorithm for swine tracking. It encompasses the development of a pig target detection model based on RetinaNet and introduces an innovative strategy for swine trajectory tracking incorporating time-series information, facilitating real-time tracking of multiple pig targets. The results from algorithm testing demonstrated the effectiveness of the swine target detection algorithm based on RetinaNet, with an AP₅₀ of 0.998, AP₇₅ of 0.907, AP₉₀ of 0.606, and an operational speed of 42.3 tasks per second. This underscored the algorithm's capacity to proficiently detect pig target categories and delineate precise target bounding boxes. In terms of swine target detection, the multi-object trajectory tracking algorithm achieved an average Multi-Object Tracking Precision of 2.37 pixels, equivalent to approximately 1.83 cm in distance. Furthermore, it attained an average Multi-Object Tracking Accuracy of 97.44%, thus substantiating its aptitude for the effective tracking of multiple pig targets with an exceptional level of tracking precision and consistency.

Proceedings Article | 29 January 2024 Paper

An application of the color spatio-temporal decomposition in video series of sign plate recognition

Dan Li, Muhan Xue, Yifei Chen, Yu Wang, Shuo Xu, Feng Lu, Guohui Cui

Proceedings Volume 12984, 1298403 (2024) https://doi.org/10.1117/12.3017904

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Marker recognition is vital in machine vision and applicable within many fields, such as vehicle automatic guidance, insect pest estimation, and UAV trajectory planning. The influence of illumination and complex backgrounds make such recognition applications very challenging. This paper describes a color Spatio-temporal decomposition algorithm as applied to video images to recognize markers. In the proposed method, the Vectorial Rudin-Osher-Fatemi model weakens the textural component of the image sequences to minimize background complexity for image segmentation. The impact of illumination is reduced by transforming the color space of the obtained image sequences into HSV and equalizing the histogram for the Value channel. Three different types of markers were tested under different light intensities and environments to verify the effectiveness of the algorithm. The proposed method improved the accuracy of edge detection in image segmentation and successfully minimized the interference of illumination. The algorithm also showed favorable good robustness under various vegetation density environments, with a recognition rate of about 95%.

Proceedings Article | 19 October 2023 Paper

Single pig pose estimation using cross-stage stacked hourglass network

Minjuan Wang, Wanwan Li, Wenxin Zhang, Guixin Li, Kun Dong, Lihua Zheng, Yifan He, Yifei Chen

Proceedings Volume 12709, 127090F (2023) https://doi.org/10.1117/12.2685014

KEYWORDS: Pose estimation

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Posture changes in pig behaviors are a helpful way of detecting early signs of precision livestock farming. In commercial settings, computer vision-based approaches have been widely used to obtain individual pig health and welfare information such as body condition score, live weight, and activity behaviors. For this, precisely estimating pig posture is a prerequisite, which is an important step in obtaining real-time pig information. In this paper, a cross-stage stacked hourglass network is conducted to solve pig posture estimation in a real feedlot environment. In addition, a cross-stage connection method that increases the flow of information to mitigate possible information loss is proposed, which obtains 2.1% improvement of PCKh than that of the original network. We trained and tested the proposed approach on a challenging pig dataset, which includes not only real pig images but also images from different websites. The results indicate that it is possible to recognize pig posture from images without any manual interference automatically, and it has great potential for application in the early detection of health and welfare challenges of commercial pigs.

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