Glass bottles must be thoroughly inspected before they are used for packaging. However, the vision inspection of bottle bottoms for defects remains a challenging task in quality control due to inaccurate localization, the difficulty in detecting defects in the texture region, and the intrinsically nonuniform brightness across the central panel. To overcome these problems, we propose a surface defect detection framework, which is composed of three main parts. First, a new localization method named entropy rate superpixel circle detection (ERSCD), which combines least-squares circle detection and entropy rate superpixel (ERS) with an improved randomized circle detection, is proposed to accurately obtain the region of interest (ROI) of the bottle bottom. Then, according to the structure-property, the ROI is divided into two measurement regions: central panel region and annular texture region. For the former, a defect detection method named frequency-tuned anisotropic diffusion super-pixel segmentation (FTADSP) that integrates frequency-tuned salient region detection (FT), anisotropic diffusion, and an improved superpixel segmentation is proposed to precisely detect the regions and boundaries of defects. For the latter, a defect detection strategy called wavelet transform multiscale filtering (WTMF) based on a wavelet transform and a multiscale filtering algorithm is proposed to reduce the influence of texture and to improve the robustness to localization error. The proposed framework is tested on four data sets obtained by our designed vision system. The experimental results demonstrate that our framework achieves the best performance compared with many traditional methods.

Glass bottles are widely used as containers in the food and beverage industry, especially for beer and carbonated beverages. As the key part of a glass bottle, the bottle bottom and its quality are closely related to product safety. Therefore, the bottle bottom must be inspected before the bottle is used for packaging. In this paper, an apparatus based on machine vision is designed for real-time bottle bottom inspection, and a framework for the defect detection mainly using saliency detection and template matching is presented. Following a brief description of the apparatus, our emphasis is on the image analysis. First, we locate the bottom by combining Hough circle detection with the size prior, and we divide the region of interest into three measurement regions: central panel region, annular panel region, and annular texture region. Then, a saliency detection method is proposed for finding defective areas inside the central panel region. A multiscale filtering method is adopted to search for defects in the annular panel region. For the annular texture region, we combine template matching with multiscale filtering to detect defects. Finally, the defect detection results of the three measurement regions are fused to distinguish the quality of the tested bottle bottom. The proposed defect detection framework is evaluated on bottle bottom images acquired by our designed apparatus. The experimental results demonstrate that the proposed methods achieve the best performance in comparison with many conventional methods.

Surface extraction plays a significant role in blade reconstruction and measurement, improving manufacturing quality and reducing costs. Boundary extraction and region growth are the most common solutions. However, these methods treat points as complete objects and extract surfaces aggressively. Since the model and geometric constraints of the surfaces are usually blurring, these methods will lead to low accuracy results and dramatically impact the effectiveness of the existing methods in practice. This article presents a novel bottom-up irregular curvature surface extracting method called SuCMIS segmenting blade surface points from an unstructured scanning point cloud. SuCMIS applies adaptive multiple clustering methods based on the local flatness and normal deviation minimum, which improves the robustness to noise and unpredicted systematic errors. Then the curvature-based cluster merging process runs to reconstruct the blade surface. Different from the existing model-based method, SuCMIS converts surface extraction into a bottom-up process with multiply surfaces’ clustering and merging. It does not rely on evaluating the accuracy of the model and geometric constraints, which overcomes the natural defects of the existing methods significantly. Experiments demonstrate SuCMIS’s efficiency and robustness. Compared with baseline methods, SuCMIS improves extracting accuracy with acceptable execution time, especially for complicated and large point clouds, such as blade surfaces.

Grasp pose detection is crucial in robotics and automation, but generating reliable and humanoid grasps in unstructured environments remains a challenge. Deep learning neural networks have gained attention in computer vision, leading to the development of grasp detection methods. Existing methods generate multiple potential grasp poses, but in practical applications, only one suitable grasp position is typically chosen, resulting in wasted resources and decreased effectiveness. Moreover, these methods often suffer from computational overhead. In this paper, we propose an efficient grasp pose detection method based on Point-PN to solve these problems. We are able to generate a single optimal grasp pose for objects in the scene by decoupling the grasp pose prediction task into subtasks of grasp approach vector, grasp rotation angle, and grasp depth, and evaluating the pose quality using grasp confidence scores. Additionally, We introduce a multi-structure network that utilizes Point-PN as the network backbone, enabling efficient learning of point cloud features and reducing training costs and resource consumption. For our network, we proposed a joint loss function in order to better adapt to the various grasp pose prediction subtasks. Experimental results demonstrate significant achievements in network evaluation and real robot experiments.