Wooden spoon crack detection by prior knowledge-enriched deep convolutional network

Since the outbreak of COVID-19, in order to reduce people's contact, the takeaway business has been developed rapidly, bringing a large demand for disposable and degradable tableware (e.g., wooden spoon). However, in the production process of wooden spoon, the selection of crack spoons still relies on manual labour. Therefore, in order to detect cracked wooden spoons more effectively and reduce production costs, we propose a wooden spoon crack detection method by using machine vision techniques and apply it in real-world industrial factory. In the production system, the captured color of crack regions is black while the good region shows normal log color. The positions of crack regions are located frequently in the central or marginal areas of spoons and their directions of cracks are often same due to the extrusion of the mold in the production process. Based on these two types of prior knowledge (i.e., color and spatial prior information), three modules are designed to explore these priors by jointly integrating with the current mainstream detection network of YOLO-v5, which satisfies the speed and accuracy for detecting cracks. The color fusion module is designed to explore the color difference between good regions and crack regions. The attention and orientation modules are then combined and embedded into the backbone of deep architecture. Reported experiments on our collected database show that our proposed detection method can locate the spoon cracks very well and significantly outperforms the model of YOLO-v5 with the protocols of Recall, Precision and meanAveragePrecision(mAP).