Ms. Mengsi Li Profile

Mengsi Li

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Proceedings Article | 21 June 2024 Paper

Particle size estimation of industrial raw materials based on improved YOLOv7

Feiyang Zhang, Ziming Zhu, Rentao Zhao, Mengsi Li

Proceedings Volume 13167, 1316712 (2024) https://doi.org/10.1117/12.3029708

KEYWORDS: Particles, Raw materials, Detection and tracking algorithms, Education and training, Target detection, Small targets, Computed tomography, Algorithm development, Image segmentation, Image processing

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At present, the traditional particle size estimation of industrial raw materials mainly relies on manual measurement and observation, which has the problems of poor accuracy and low efficiency, etc. In this paper, an automatic image-based particle size estimation method for industrial raw materials is proposed, and the YOLOv7 target detection algorithm is used to classify and detect the particles. In YOLOv7, segmentation module, Biformer attention mechanism and NWD (Normalized Gaussian Wasserstein Distance) loss function calculation method are introduced to solve the problems of the original algorithm's prediction frame is large or skewed, the speed is slow when detecting the dense and small targets, and the detection accuracy is low. The problem of the original algorithm is solved. In this paper, two particle size estimation statistical methods are proposed to classify and count the particle size of industrial raw material particles. The test results show that the improved algorithm achieves 99.5% mAP on the gravel dataset, 90.8% mAP on the industrial raw material particles dataset, and 92.7% mAP on the mixed dataset, and the prediction of segmentation of a picture with hundreds of dense small targets only takes 27.2ms, which can meet the real-time and accurate industrial raw material particle size statistics in the industrial field, and it has a greater Practical value.

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