GMS-YOLO: an enhanced algorithm for water meter reading recognition in complex environments

IF 2.9 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yu Wang, Xiaodong Xiang
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Abstract

The disordered arrangement of water-meter pipes and the random rotation angles of their mechanical character wheels frequently result in captured water-meter images exhibiting tilt, blur, and incomplete characters. These issues complicate the detection of water-meter images, rendering traditional OCR (optical character recognition) methods inadequate for current detection requirements. Furthermore, the two-stage detection method, which involves first locating and then recognizing, proves overly cumbersome. In this paper, water-meter reading recognition is approached as an object-detection task, extracting readings using the algorithm’s Predicted Box information, establishing a water-meter dataset, and refining the algorithmic framework to improve the accuracy of recognizing incomplete characters. Utilizing YOLOv8n as the baseline, we propose GMS-YOLO, a novel object-detection algorithm that employs Grouped Multi-Scale Convolution for enhanced performance. First, by substituting the Bottleneck module’s convolution with GMSC (Grouped Multi-Scale Convolution), the model can access various scale receptive fields, thus boosting its feature-extraction prowess. Second, incorporating LSKA (Large Kernel Separable Attention) into the SPPF (Spatial Pyramid Pooling Fast) module improves the perception of fine-grained features. Finally, replacing CIoU (Generalized Intersection over Union) with the ShapeIoU bounding box loss function enhances the model’s ability to localize objects and speeds up its convergence. Evaluating a self-compiled water-meter image dataset, GMS-YOLO attained a mAP@0.5 of 92.4% and a precision of 93.2%, marking a 2.0% and 2.1% enhancement over YOLOv8n, respectively. Despite the increased computational burden, GMS-YOLO maintains an average detection time of 10 ms per image, meeting practical detection needs.

Abstract Image

GMS-YOLO:复杂环境下水表读数识别的增强算法
水表管道的无序排列及其机械字符轮的随机旋转角度经常导致捕捉到的水表图像显示出倾斜、模糊和不完整的字符。这些问题使得水表图像的检测变得复杂,传统的 OCR(光学字符识别)方法无法满足当前的检测要求。此外,先定位再识别的两阶段检测方法也过于繁琐。本文将水表读数识别作为一项对象检测任务,利用算法的预测框信息提取读数,建立水表数据集,并改进算法框架,以提高识别不完整字符的准确性。以 YOLOv8n 为基线,我们提出了 GMS-YOLO,一种采用分组多尺度卷积以提高性能的新型物体检测算法。首先,通过用 GMSC(分组多尺度卷积)替代瓶颈模块的卷积,该模型可以访问各种尺度的感受野,从而提高其特征提取能力。其次,将 LSKA(大核可分离注意力)纳入 SPPF(空间金字塔池化快速)模块,提高了对细粒度特征的感知能力。最后,用 ShapeIoU 边框损失函数取代 CIoU(广义联合相交),增强了模型定位物体的能力,并加快了收敛速度。在评估自编的水表图像数据集时,GMS-YOLO 的 mAP@0.5 和精度分别达到了 92.4% 和 93.2%,比 YOLOv8n 分别提高了 2.0% 和 2.1%。尽管增加了计算负担,GMS-YOLO 每幅图像的平均检测时间仍保持在 10 毫秒,满足了实际检测需要。
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来源期刊
Journal of Real-Time Image Processing
Journal of Real-Time Image Processing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
6.80
自引率
6.70%
发文量
68
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.
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