New Common Rail Valve F00VC01362 for Injector 0445110302 0445110303 for Injection Needle

Due to the rapid development of society, automobiles have become an increasingly important travel tool in daily life. As a device for injecting gasoline into automobile cylinders, the valve seat of automobile injectors plays a very important role in fuel quantity control. How to improve the quality of the parts has become an important issue of concern, but because of the small size of parts, it is easy to be limited by the processing technology. During the production process, it will inevitably leave scratches, defect, rust spots, white spots and other types of defects inside, which affects the performance of the automotive injector seat.

Therefore, picking out defective parts from many parts has become an inevitable project. With the rapid increase of image data and the rapid progress of hardware computing ability, the deep learning detection technology, represented by convolutional neural network, has been applied to the related tasks of flaw detection. Compared with the traditional algorithm, the performance has been greatly improved. In 2014, Ross Girshick [1] and others proposed the R-CNN algorithm to extract candidate regions through a selective search algorithm, but the algorithm is computationally intensive and slow. Subsequently, the target detection algorithm SPP-Net is proposed, which solves the problem of object deformation, and then Fast R-CNN is proposed by introducing multi-task loss and RoI Pooling, which uses multi-task learning to complete classification and regression.

However, the regional method adopted by the algorithm will still consume a lot of time. Therefore, Ren [2] proposed the Faster R-CNN algorithm. The algorithm introduces the RPN network on the basis of the Fast R-CNN algorithm, which has been greatly improved in speed and performance. The Faster R-CNN algorithm can achieve better results in object detection than other algorithms.