Vehicle recalls can be extremely damaging for automotive manufacturers. The average cost of recalling a vehicle is around $500. That means that when tens of thousands of cars have to be recalled to fix serious defects it can end up costing companies hundreds of millions of dollars.
While many recalls are due to design errors, defects in manufacturing can also lead to unsatisfactory or dangerous vehicles being returned to the dealer. As a result automakers are constantly looking to improve quality control at their manufacturing sites.
Increasingly, innovators in the sector are recognising the potential of computer vision annotation tools to improve manufacturing quality. Machine learning can create models that are capable of levels of precision that exceed human inspectors.
Today's blog will look at 5 key ways in which computer vision based AI models and machine learning algorithms can help manufacturers safeguard quality and avoid costly recalls.
Detect defects with computer vision
Rigorous quality control is essential for preventing recalls. Finding defects in parts and fittings requires attention to detail and rock solid QA processes. However, some defects can be too small for the human eye to register. Minute cracks or imperfection in circuit boards or other vital components can lead to problems down the line that require recalls.
Computer vision systems, AI project management tools are being introduced to cover this gap. AI powered systems, using image annotation tool and video annotation tool can inspect parts with high levels of precision 24/7. AI research teams train models with annotated images, where defects are highlighted. This enables AI models and data labeling tools to spot defects reliably in the real world.
Protect vital equipment with high-level training dataset
Faulty manufacturing equipment can also lead to mistakes in production. Poorly calibrated or defective machinery can reduce the quality of manufactured parts and lead to issues that might require a recall. To avoid this, auto companies are turning to predictive maintenance AI technology and machine learning models.
Predictive maintenance is the process of assessing when a piece of equipment will need to be repaired, avoiding equipment down time. AI can be used to continuously monitor manufacturing equipment and improve predictive maintenance. This can help improve the efficiency of maintenance processes because repairs are only being carried out when necessary.
Prevent accidents on the production line
Car manufacturers are beginning to introduce autonomous robots to their production lines. This technology promises to improve manufacturing quality whilst reducing costs. However, smart robotic arms mean that safety is a priority on the production line.
Object recognition allows AI driven robots to label images and avoid obstacles and potentially dangerous collisions in dynamic manufacturing environments. Precisely annotated video data is vital for reliable object detection. With the right training autonomous manufacturing robots can streamline production of vehicles and reduce recalls.
Improve inventory management with AI monitoring
Eliminating recalls requires a holistic approach to the manufacturing process. Keeping track of inventory is an important part of creating a mistake free vehicle production environment.
Computer vision based AI models are particularly well equipped to monitor inventory levels at warehouses and other installations. AI powered cameras can recognize types of stock and provide information and warnings when levels of a particular part or item begin to drop.
Find the right data annotation tool
The auto industry is waking up to the capacity of AI to improve manufacturing quality, eliminate defects, and prevent expensive vehicle recalls. However, AI developers need high quality training data to continue improving their models.
Keylabs is tool designed by annotation experts. Unique project management features allow AI researchers to guide annotation projects and ensure that they get the most precise image and video data possible. And task sharing and interpolation options guarantee that even the most challenging labeling tasks will be accomplished on schedule.