During Printed Circuit Board (PCB) manufacturing, it is critical to dispense the correct amount of conductive glue on the substrate LCP surface before die attachment, as the dispensing of excessive or insufficient glue may cause defects through short circuits or weak die bonding. Therefore it is critical to monitor the amount of the dispensed glue during production.


LiDAR point cloud data serves as an machine vision alternative other than image. Its advantages when compared to image and video includes depth estimation and distance measruement. Low-density LiDAR point cloud data can be used to achieve navigation, obstacle detection and obstacle avoidance for mobile robots. autonomous vehicle and drones. In this metadata, we scanned over 1200 objects and classified it into 4 groups of object namely, human, cars, motorcyclist.


This paper aims to improve the existing techniques on X-ray image inspection of aerial engine by using artificial intelligence (AI) based object detection model. This technique seeks to augment and improve existing automated non-destructive testing (NDT) diagnosis of metal structure of engine parts. Traditional jet-engine maintenance and overhaul processes are resorted to NDT to find defects in internal welds. An application of deep learning for NDT technology can effectively identify presence and location of up to eight types of defects, leading to enhanced work quality and efficiency.


This tool model propose use deep learning edge detection to improve non-destructive radiographic tests of engine cracks by employing in deep unsupervised learning on RT architecture. he engine repair plant has accumulated a data gallery of X-ray images. In the dataset, defects in engine can be categorized as either (a) cracks, (b) incomplete fusion, (c) incomplete penetration, (d) porosity, (e) slag inclusion, (f) undercut, (g) welding spatter, or (h) blowhole as depicted in Figure 4. The X-ray images in the dataset are converted into LMDB format and stored for deep learning application. In the dataset, some X-ray images in different light conditions and resolutions have been labeled with the above defects. An effort is made to prepare the labeled data in terms of DetectNet model. In this endeavor, the 8 types of label classes are stored in the first row of the category table string over 6,000 labeled X-ray images dataset.