Automating visual inspection with AI
Artificial intelligence is now making inroads into the manufacturing sector. This article discusses how AI is utilized in automated visual evaluation to enhance manufacturing processes.
Artificial intelligence is proving to be a game-changer, with numerous applications in almost every sector. It is now finding its way into Production and Manufacturing, where it will leverage the potential of deep learning. This post will provide an overview of automated visual evaluation and how a deep learning method may save substantial time and effort.
Visual inspection is a quality control technique that involves the evaluation of assets. It is mainly used for facility maintenance and inspection of equipment and structures using any or all of the raw human senses or specialised equipment.
Defective materials impair the manufacturing process's efficiency. They also generate waste and additional output that falls short of consumer expectations. In addition, the existence of faulty materials necessitates the use of time-consuming quality assurance methods. Overall, product faults raise production costs as well as operating and maintenance costs.
Defects occur in all production contexts, from consumer items to industrial commodities and built structures. In this example, we look at the issues arising from defects in urban roadways.
Potholes, surface degradation, edge failure, and cracking are all examples of flaws that may occur on our roadways. It eventually causes road degradation, resulting in an uncomfortable driving experience, automobile damage, and road accidents. Unfortunately, the current procedure for identifying these defects involves manual inspections, which are time-consuming and labour-intensive tasks for maintenance engineers in charge of road quality assurance.
Consequently, it is impossible to ensure rapid road surface repairs, resulting in a range of expenses, including direct costs (labour and inspection) and liability costs (damage to cars, road accidents).
Artificial intelligence (AI) aids manufacturers in significantly increasing the efficiency and efficacy of detecting all types of defects. Maintenance engineers and quality controllers may prevent defects from getting past their screening methods by utilising advances in computer vision while still minimising the resources required.
Computer vision will learn the patterns in photographs of your items to reliably distinguish which are defective, similar to how people learn to recognise patterns from visual cues.
With advancements in AI usability, we can now train and deploy models on images of defective products and incorporate them into your monitoring system. Your quality inspectors may then monitor and manage the detections from a simple web interface and respond accordingly.
In a recent case study, we trained an AI model on selected Ghanaian highways using the Yolo object detection algorithm. This study aims to develop road visual inspection AI systems that can detect defects like potholes and fractures. We successfully tested the trained model by deploying our model on a drone to detect road problems in real-time. The above video shows the model's prediction on sampled videos of the road. The model successfully identified all potholes on the road.
As previously stated, the AI model demonstrated here for detecting road damage can be used to discover defects in any product or component. The following are some examples of how the AI model:
- Textile industry: Detect issues of texture, weaving, stitching, and color matching.
- Automotive industry: Detect cracks, scratch, dirt and dent on cars and automobile parts.
- Factories: Identify defective products on your assembly line.
- Building materials: Detect cracks, scratch, dirt, dent and surface pattern on wood boards, metal fitting, and tiles.
- Electronic parts: Detect cracks, scratch, chip on PCB, electronic parts, panel etc.
- Food: Detect a foreign object, wrong print on processed foods and beverages.