ViMo Cloud is an AI full-process platform designed for industrial vision applications, providing a one-stop solution that includes project management, data management, scheme design, and model building. Relying on SmartMore's industry-leading self-developed algorithms as its core foundation, ViMo Cloud offers a plug-and-play algorithm toolset that can effectively solve problems such as material tracking, defect location, workpiece counting, and appearance defect detection on production lines. The cloud-based deployment of the platform further reduces the cost of computing power resources, helps realize low-threshold construction of visual models with efficient iteration and rapid deployment, empowers multi-level business scenarios, and accelerates the implementation of industrial AI applications.
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Precision Grinding Surface Defect Detection for Steering Joints at a Global Top 500 Japanese Automotive Parts Manufacturer
The automotive steering knuckle forging process is complex and prone to forging defects, resulting in defective or even scrap products, causing significant financial losses for the company. If these products enter the market, they can directly impact the occurrence rate of traffic accidents. Therefore, the manufacturer needs to develop the capability to quickly identify and eliminate forging defects on the automated production line.
Based on the core algorithm capabilities of SMore ViMo Intelligent Industrial Platform, SmartMore has provided a comprehensive solution for precision grinding surface inspection of steering knuckles to the customer. With the Vi-Lab operation management system, SmartMore enables the customer to quickly build a machine vision system and deploy it rapidly. The hardware components include light sources, cameras, etc. This solution achieves an accuracy rate of over 95%, resulting in a cost savings of 60% for the customer. SmartMore has successfully implemented the first automated intelligent quality inspection project for this manufacturer, serving as a successful pilot project.
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USB Defect Detection for a Well-known Consumer Electronics Manufacturer
The project requires a three-class classification (OK/NG/NG-2) of scratches and dirt on the USB interface. Previously, the customer's quality inspection was mainly carried out through manual visual inspection, which resulted in high costs and low detection efficiency. Additionally, the tested products have various types of defects, requiring precise defect classification based on their characteristics during inspection. Traditional computer vision algorithms cannot establish standard detection templates for some defects, necessitating the use of high-precision deep learning algorithms for detection.
Based on the high-precision algorithm capability of SMore ViMo, SMIoT can perform multiple defect detections on products with an accuracy and recall rate of up to 98.9%. The false negative rate is below 2%, enabling the realization of fully automated production lines. This solution effectively reduces production inspection costs, improves product yield, and ensures operational efficiency.
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OCR Smartwatches from a Top Global Smartphone Manufacturer
The project requires the recognition of characters engraved on metal components of smartwatches. There are three different fonts for the characters, and the strap can come in black, white, or orange colors. The imaging conditions are complex, including various forms such as blurriness, lighting variations, and tilting, presenting significant challenges for recognition.
Based on SMore ViMo's industrial optical character recognition algorithm, SMore has developed a sophisticated preprocessing approach to enhance the algorithm's robustness to environmental variations. This approach ensures a high character recognition accuracy of up to 99.9% and enables compatibility with various product configurations, As a result SMore has successfully implemented fully automated production on six production lines for our customers.
