Defect Detection of Industry Wood Veneer Based on NAS and Multi-Channel Mask R-CNN

Defect Detection of Industry Wood Veneer Based on NAS and Multi-Channel Mask R-CNN

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Wood veneer defect detection plays a vital role in the wood veneer production industry.Studies on wood veneer defect detection NTSC to PAL Convertor usually focused on detection accuracy for industrial applications but ignored algorithm execution speed; thus, their methods do not meet the required speed of online detection.In this paper, a new detection method is proposed that achieves high accuracy and a suitable speed for online production.

Firstly, 2838 wood veneer images were collected using data collection equipment developed in the laboratory and labeled by experienced workers from a wood company.Then, an integrated model, glance multiple channel mask region convolution neural network (R-CNN), was constructed to detect wood veneer defects, which included a glance network and Window Storage a multiple channel mask R-CNN.Neural network architect search technology was used to automatically construct the glance network with the lowest number of floating-point operations to pick out potential defect images out of numerous original wood veneer images.

A genetic algorithm was used to merge the intermediate features extracted by the glance network.Multi-Channel Mask R-CNN was then used to classify and locate the defects.The experimental results show that the proposed method achieves a 98.

70% overall classification accuracy and a 95.31% mean average precision, and only 2.5 s was needed to detect a batch of 50 standard images and 50 defective images.

Compared with other wood veneer defect detection methods, the proposed method is more accurate and faster.