According to a recent study, defective products cost industries over $2 billion from 2012–2017. Defect detection within manufacturing is an important business use case, especially in high-value product industries like the automotive industry. This allows for early diagnosis of anomalies to improve production line efficacy and product quality, and saves capital costs. Although advanced anomaly detection systems employ sensors as well as Internet of Things (IoT) devices to collect multimodal data to improve performance, computer vision continues to be a common approach. Detecting anomalies in automotive parts and components using computer vision can be done using normal images, and even X-Ray based images for structural damages. Recent advances in deep learning and computer vision have allowed scientists and manufacturers to develop enhanced anomaly detection systems, including surface defect detection on automotive body panels and dent detection in vehicles.
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