Real-Time Recognition Method for 0.8 cm Darning Needles and KR22 Bearings Based on Convolution Neural Networks and Data Increase

Abstract

The complexity of the background and the similarities between different types of precision parts, especially in the high-speed movement of conveyor belts in complex industrial scenes, pose immense challenges to the object recognition of precision parts due to diversity in illumination. This study presents a real-time object recognition method for 0.8 cm darning needles and KR22 bearing machine parts under a complex industrial background. First, we propose an image data increase algorithm based on directional flip, and we establish two types of dataset, namely, real data and increased data. We focus on increasing recognition accuracy and reducing computation time, and we design a multilayer feature fusion network to obtain feature information. Subsequently, we propose an accurate method for classifying precision parts on the basis of non-maximal suppression, and then form an improved You Only Look Once (YOLO) V3 network. We implement this method and compare it with models in our real-time industrial object detection experimental platform. Finally, experiments on real and increased datasets show that the proposed method outperforms the YOLO V3 algorithm in terms of recognition accuracy and robustness.

Detecting Gear Surface Defects Using Background-Weakening Method and Convolutional Neural Network

“…where C is the confidence score;Ĉ is the intersection of the predicted bounding box and the basic fact, when an object exists in a cell; and ℓ obj ij is equal to 1; otherwise, it is 0; λ noobj represents the confidence weight when no object exists in the bounding box [10].…”

“…Online Platform for Industrial Defect Detection Figure 4 depicts the system flow chart of the online testing platform for gear manufacturing defects designed by the research group. Figure 5 is an online test platform for gear manufacturing defects built by the research team [10,32]. This platform includes the conveyor belt, data processor, data acquisition sensor, light source, and other mechanical supports, wherein the touch display for inputting and displaying data is the 32-inch industrial touch screen.…”

“…With the rapid development of deep learning in daily life [5][6][7] and industrial fields [8,9], many scholars attempt to apply deep learning methods for detecting part defects [10]. To ensure the quality of online defect detection, the network must exhibit fast positioning speed and high classification accuracy.…”

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