An “object detection model” is an advanced artificial intelligence model tailored for visual tasks such as image recognition, object detection, segmentation, and image generation. Object detection models utilize Vision Transformers and are trained on extensive datasets of images and videos to learn pattern recognition, object classification, and visual content generation. Vision Transformer architectures enable these models to generalize knowledge from vast training data effectively, resulting in superior performance in few-shot and zero-shot inference across various downstream tasks.

Custom object detection models are deep learning systems that excel in analyzing visual data with exceptional precision. Trained on extensive datasets, these models can identify patterns, detect anomalies, and recognize specific features within images. In QC, custom object detection models offer several key advantages:

  • Precision: They deliver higher accuracy than human inspectors by eliminating subjective biases and minimizing errors.
  • Speed: These models can analyze images in real-time, accelerating inspection processes.
  • Consistency: Custom object detection models provide uniform results, ensuring that quality standards are consistently met.
  • Model Structure: To tackle this problem, we developed a custom object detection model with 50k adjustable parameters. These parameters enable the model to learn the relationship between switch icons and the target classes in the training data. A model with more parameters can handle greater complexity and process more data. However, the increased number of parameters also makes the model more computationally intensive to train and deploy.


  • Training the Custom Object Detection Model:



    • Training a custom object detection model is more challenging than it seems, requiring substantial GPU power and intensive training with a large dataset. We used approximately 10k real-time images, capturing various switch dashboards from different trucks. This custom model is specifically trained for the inspection of switches and icons using a vast number of images and videos, making it a domain-specific model.

Our custom object detection model, designed for visual inspection and verification tasks, is created using a FastAPI web server and deployed on an Amazon EC2 t2.micro instance. This setup leverages the scalable and reliable infrastructure of EC2 to ensure real-time processing and analysis. The Fast API framework provides a high-performance web interface for interacting with the model, making it accessible and efficient for deployment in various industrial quality control applications.

When evaluating the performance of our custom object detection model against other object detection models, the results clearly demonstrate the superiority of our model in terms of accuracy and efficiency, particularly in the context of switch and icon verification on truck dashboards.

  • Accuracy and Efficiency Our custom object detection model achieves superior accuracy compared to traditional object detection models like SSD, and Faster R-CNN. It excels in real-time applications, ensuring rapid and precise switch and icon verification on truck dashboards without compromising on speed.
  • Robustness Across Diverse Condition The custom model is designed to handle the varied and challenging conditions often encountered in real-world scenarios, such as different lighting and occlusions. Its domain-specific training enables it to maintain high performance and reliability, outperforming conventional models in robustness and adaptability.

We have established an extensive benchmark for this performance test, comparing our model with other leading models. As a result, we can now complete a full truck inspection within 5 minutes, compared to the previous 15 minutes. This improvement allows truck companies to inspect many trucks in significantly less time, without any drop in accuracy or performance.

AI Visual Inspection Solutions

Custom object detection models represent a significant advancement in quality control for industrial manufacturing. By harnessing the power of AI and machine learning, these models offer unmatched accuracy, speed, and consistency in defect detection and anomaly identification. The applications in verifying switch uniformity in trucks and detecting carbon soot on conveyor belts highlight the transformative potential of these models. As these technologies continue to advance, their integration into QC processes will lead to higher product quality, increased efficiency, and enhanced operational reliability.

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