ViMo Deeplearning is a desktop industrial vision deep learning training software developed by SmartMore. Leveraging the cutting-edge computer vision algorithms from ViMo Cloud, the product focuses on refining and optimizing smart annotation, automatic algorithm training, rapid model tuning, and efficient SDK inference. Additionally, it is user-friendly and requires zero programming skills, allowing users to accomplish core tasks in smart manufacturing production lines, such as material classification, defect detection, object localization, and character recognition.
As a desktop software, ViMo Deeplearning complements ViMo Cloud by providing offline model training capabilities. This achievement enables the ViMo product series to offer comprehensive coverage of capabilities in the industrial vision field, empowering more enterprises with opportunities for visual intelligence upgrades and facilitating the practical implementation of intelligent manufacturing upgrades.
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.
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.
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.
