Lightweight detection model for coal gangue identification based on improved YOLOv5s

IF 2.9 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Real-Time Image Processing Pub Date : 2024-07-23 DOI:10.1007/s11554-024-01518-5

Deyong Shang, Zhibin Lv, Zehua Gao, Yuntao Li

引用次数: 0

Abstract

Focusing on the issues of complex models, high computational cost, and low identification speed of existing coal gangue image identification object detection algorithms, an optimized YOLOv5s lightweight detection model for coal gangue is proposed. Using ShuffleNetV2 as the backbone network, a convolution pooling module is used at the input end instead of the original convolution module. Combining the re-parameterization idea of RepVGG and introducing depthwise separable convolution, a neck feature fusion network is constructed. And using the WIoU function as the loss function. The experimental findings indicate that the improved model maintains the same accuracy, the number of parameters is only 5.1% of the original, the computational effort is reduced to 6.3 % of the original, and the identification speed is improved by 30.9% on GPU and 4 times on CPU. This method significantly reduces model complexity and improves detection speed while maintaining detection accuracy.

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基于改进型 YOLOv5s 的煤矸石识别轻量级检测模型

针对现有煤矸石图像识别对象检测算法模型复杂、计算成本高、识别速度低等问题，提出了一种优化的 YOLOv5s 煤矸石轻量级检测模型。以 ShuffleNetV2 为骨干网络，在输入端使用卷积池模块代替原有的卷积模块。结合 RepVGG 的重参数化思想，引入深度可分离卷积，构建了颈部特征融合网络。并使用 WIoU 函数作为损失函数。实验结果表明，改进后的模型精度保持不变，参数数量仅为原来的 5.1%，计算量减少到原来的 6.3%，识别速度在 GPU 上提高了 30.9%，在 CPU 上提高了 4 倍。该方法在保持检测精度的同时，大大降低了模型复杂度，提高了检测速度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Real-Time Image Processing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

6.80

自引率

6.70%

发文量

审稿时长

6 months

期刊介绍： Due to rapid advancements in integrated circuit technology, the rich theoretical results that have been developed by the image and video processing research community are now being increasingly applied in practical systems to solve real-world image and video processing problems. Such systems involve constraints placed not only on their size, cost, and power consumption, but also on the timeliness of the image data processed. Examples of such systems are mobile phones, digital still/video/cell-phone cameras, portable media players, personal digital assistants, high-definition television, video surveillance systems, industrial visual inspection systems, medical imaging devices, vision-guided autonomous robots, spectral imaging systems, and many other real-time embedded systems. In these real-time systems, strict timing requirements demand that results are available within a certain interval of time as imposed by the application. It is often the case that an image processing algorithm is developed and proven theoretically sound, presumably with a specific application in mind, but its practical applications and the detailed steps, methodology, and trade-off analysis required to achieve its real-time performance are not fully explored, leaving these critical and usually non-trivial issues for those wishing to employ the algorithm in a real-time system. The Journal of Real-Time Image Processing is intended to bridge the gap between the theory and practice of image processing, serving the greater community of researchers, practicing engineers, and industrial professionals who deal with designing, implementing or utilizing image processing systems which must satisfy real-time design constraints.