Real-time defect detection and classification on wood surfaces using deep learning

Abstract

This paper proposes a novel method for automatic realtime defect detection and classification on wood surfaces. Our method uses deep convolutional neural network (CNN) based approach Faster R-CNN (Region-based CNN ) as detector and Mo-bileNetV3 as backbone network for feature extraction. The key difference of our approach from the existing methods is that it detects knots and other type of defects efficiently and does the classification in real-time from the input video frames. Speed and accuracy is the main focus of our work. In the case of the industrial quality control and inspection such as defects detection, the task of detection and classification needs to be done in real-time on a computationally limited processing units or commodity processors. Our trained model is a light weight, and it can even be deployed on systems for example mobile and edge devices. We have pre-trained the MobileNet V3 on large image dataset for feature extraction. We use Faster R-CNN for detection and classification of defects. The system does the real-time detection and classification on an average of 37 frames per second from input video frames, using low cost and low memory GPU (Graphics Processing Unit). Our method has achieved an overall accuracy of 99% in detecting and classifying defects.

Capsule Defect Detection Method Based on an Improved Model

“…In recent years, target detection techniques [7] have gained popularity in the field of capsule surface defect detection, among which the YOLO series algorithm is widely adopted. However, the YOLO series algorithm still faces issues in capsule surface defect detection, including excessive computational resource demands, large model sizes, and false or missed detections [8]. To overcome these limitations, we propose an improved model based on YOLOv5.…”