How switching from a rule-based machine vision system to an advanced computer vision solution for early-stage defect detection on the production line helped an electronics manufacturer reach 97,4% accuracy in identifying flaws and save $2.4 million annually thanks to a drop in warranty claims.
Our client is a Saudi-based electronics manufacturer producing laptops for the MEA region in partnership with one of the global tech leaders. Years earlier, we had created digital twins of their factories, so when new challenges surfaced, the client turned to us again.
The company had long moved from fully manual defect detection on the production line to an automated, non-contact product quality inspection. However, their automated optical inspection (AOI) in the conveyor video surveillance system was too demanding, rigid, and costly, posing numerous limitations.
The client needed a more flexible, scalable, and budget-wise form of computer vision at the core of their conveyor video surveillance system.
Our development team ensured the smooth shift from basic AOI to advanced computer vision.
We decided to rely on a pre-trained object detection and image classification model. Then, we had to choose between single- and two-stage detectors to perform object detection. The scale was tipped in favor of a single-stage algorithm due to its several advantages:
Having experience with various single-stage detectors, we settled on YOLO over CenterNet, SSD, RetinaNet, and FCOS, as it offers superior inference speed, ensuring truly real-time performance. Despite YOLOv10 being available, we used YOLOv8, as it was the most tested and stable version at the moment.
The client’s previous pre-computer vision system relied on a three-tier defect classification framework: “definitely defective,” “borderline,” and “definitely non-defective”. Rather than discarding the existing dataset, we capitalized on it by strengthening defect coverage and enforcing consistent, high-quality labeling.
The original dataset was limited in size, with a maximum of 200 images per defect category. In addition, different defect types weren’t represented equally. For example, issues such as missing components and incorrect placement appeared far more frequently than subtler issues like misalignment, solder defects, open circuits, or lifted leads.
To address both the small dataset size and class imbalance, we applied data augmentation techniques:
That way, we multiplied the dataset tenfold while ensuring balanced representation across all defect categories.
Given the diversity of subtle defects, high-quality labeling was a must-have. Our AI engineers focused on refining the labeling process to:
In addition, we ensured the dataset included images both with and without the target object, improving the model’s ability to distinguish true defects from background artifacts and, thus, reducing the false positive rate.
More consistent labeling enhances the reliability of bounding boxes, teaching the model to distinguish every little part and defect as a separate entity, rather than group several objects within a single bounding box.
More consistent labeling enhances the reliability of bounding boxes, teaching the model to distinguish every little part and defect as a separate entity, rather than group several objects within a single bounding box.
Models for object detection and classification like YOLO are already trained on hundreds of millions of general images. However, to accurately address the client’s specific needs, it had to undergo additional training on the updated dataset.
Instinctools’ AI engineers took an efficient approach to tackle this challenge. They resorted to transfer learning as an ML training technique to improve the model’s performance:
After that, YOLO was able to instantly detect and classify all the specific defects relevant to the client’s production line.
Defining a defect is only part of the process. Next, our dedicated team helped minimize the number of items wrongly discarded at the production stage through:
After transfer learning and threshold calibration, the model’s accuracy reached 86,5% which was already more than 15% higher than with the previous AOI system. Still, our team aimed for as close to 100% accuracy as possible.
Instinctools’ AI experts enhanced YOLO-based defect detection capabilities by applying:
With these enhancements, the new CV defect detection system consistently hits the 97,4% accuracy benchmark.
The computer vision solution was installed at one of the client’s facilities. There, it underwent further model training based on collected metadata and outputs generated by the system during operation. Those adjustments helped align the system with real-world production conditions and replaced AOI at each critical juncture:
As the solution features cloud connectivity, our team can remotely perform model updates, configuration changes, and CV algorithm optimizations.
Every collaboration with *instinctools takes our factories to the next level. The new computer vision system for defect detection reimagined our quality control at production lines. The drop in discarded items below 0,5% alone fully justified the investment, but a year post-implementation, we also saw that it translated into a millions-worth reduction in warranty claims, exceeding our boldest expectations.
Stronger quality-control systems on a production line lead to fewer defective products reaching customers and ruining their experience. With the stakes as high as one in three customers* leaving a brand after a single bad experience involving defective products, playing it safe becomes essential for business survival.
Computer vision can be the key to achieving higher-quality products and improved customer satisfaction.
*According to PwC
