An Intelligent Classification System For Aggregate Based On Image Processing And Neural Network

Bentuk dan tekstur permukaan aggregat mempengaruhi kekuatan dan struktur konkrit. Secara tradisi, mesin pengayakan mekanikal dan pengukuran manual digunakan bagi menentukan kedua-dua saiz dan bentuk aggregat. Aggregate’s shape and surface texture immensely influence the strength and structure of the resulting concrete. Traditionally, mechanical sieving and manual gauging are used to determine both the size and shape of the aggregates

A comparative study on the performance of neural networks in visual guidance and feedback applications

Vision-based systems increase the flexibility of industrial automation applications by providing non-touching sensory information for processing and feedback. Artificial neural networks (ANNs) help such conformities through prediction in overcoming nonlinear computational spaces. They transform multiple possibilities of outcomes or regions of uncertainty posed by the system components towards solution spaces. Trained networks impart a certain level of intelligence to robotic systems. This paper discusses two applications of machine vision. The 3 degrees of freedom (DOF) robotic assembly provides an accurate cutting of soft materials with visual guidance using pixel elimination. The 6-DOF robot combines visual guidance from a supervisory camera and visual feedback from an attached camera. Using a switching approach in the control strategy, pick and place applications are carried out. With the inclusion of ANN to make the strategies intelligent, both the systems performed better with regard to computational time and convergence. The networks make use of the extracted image features from the scene for different applications. Simulation and experimental results validate the proposed schemes and show the effectiveness of ANN in machine vision applications

Automated computer vision system for real-time drilling cuttings monitoring

In rotary drilling operations, cuttings are continuously transported to the surface by drilling fluid. Real-time monitoring of cuttings and cavings is crucial for early detection and remediation of drilling problems such as stuck pipe, lost circulation, high torque and drag, reduction in rate of penetration, and other wellbore instability issues. These incidents are large contributors to drilling-related Non-Productive Time (NPT). At the current stage, a mud logger performs monitoring manually. This work proposes to use computer vision techniques to automate this procedure. To achieve this application, specific requirements should be established to design an automated machine vision system to maintain drilling safety and speed. Cuttings ramp has been identified as an ideal location to perform the measurement, where cuttings and caving are sliding down a slope at a steady speed. To accomplish this task, an intelligent image processing system must be able to track cuttings speed, measure volume, analyze size, and generate a surface model. Through a detailed review and testing of available 3D sensing techniques, a system consisting of a 2D high-resolution camera and 3D laser profile scanner was designed. By implementing image processing techniques, the cuttings speed on the ramp was estimated which was then synchronized to the 3D depth data from a laser scanner. Finally, the volume of moving cuttings was estimated and a 3D surface profile was reconstructed using point cloud data. Experimental results in the lab environment validated that such a system can be applied to quantify cuttings volume, size distribution, and reconstruct a 3D profile of cuttings and cavings. This measured result can be stored for further analysis. Overall, this work established a foundation for the design of a sophisticated real-time monitoring system for hole cleaning and wellbore risk reduction.Petroleum and Geosystems Engineerin

PackIt: A Virtual Environment for Geometric Planning

The ability to jointly understand the geometry of objects and plan actions for manipulating them is crucial for intelligent agents. We refer to this ability as geometric planning. Recently, many interactive environments have been proposed to evaluate intelligent agents on various skills, however, none of them cater to the needs of geometric planning. We present PackIt, a virtual environment to evaluate and potentially learn the ability to do geometric planning, where an agent needs to take a sequence of actions to pack a set of objects into a box with limited space. We also construct a set of challenging packing tasks using an evolutionary algorithm. Further, we study various baselines for the task that include model-free learning-based and heuristic-based methods, as well as search-based optimization methods that assume access to the model of the environment. Code and data are available at https://github.com/princeton-vl/PackIt.Comment: Accepted to ICML 202

Curve-Based Shape Matching Methods and Applications

One of the main cues we use in our everyday life when interacting with the environment is shape. For example, we use shape information to recognise a chair, grasp a cup, perceive traffic signs and solve jigsaw puzzles. We also use shape when dealing with more sophisticated tasks, such as the medical diagnosis of radiographs or the restoration of archaeological artifacts. While the perception of shape and its use is a natural ability of human beings, endowing machines with such skills is not straightforward. However, the exploitation of shape cues is important for the development of competent computer methods that will automatically perform tasks such as those just mentioned. With this aim, the present work proposes computer methods which use shape to tackle two important tasks, namely packing and object recognition. The packing problem arises in a variety of applications in industry, where the placement of a set of two-dimensional shapes on a surface such that no shapes overlap and the uncovered surface area is minimised is important. Given that this problem is NP-complete, we propose a heuristic method which searches for a solution of good quality, though not necessarily the optimal one, within a reasonable computation time. The proposed method adopts a pictorial representation and employs a greedy algorithm which uses a shape matching module in order to dynamically select the order and the pose of the parts to be placed based on the “gaps” appearing in the layout during the execution. This thesis further investigates shape matching in the context of object recognition and first considers the case where the target object and the input scene are represented by their silhouettes. Two distinct methods are proposed; the first method follows a local string matching approach, while the second one adopts a global optimisation approach using dynamic programming. Their use of silhouettes, however, rules out the consideration of any internal contours that might appear in the input scene, and in order to address this limitation, we later propose a graph-based scheme that performs shape matching incorporating information from both internal and external contours. Finally, we lift the assumption made that input data are available in the form of closed curves, and present a method which can robustly perform object recognition using curve fragments (edges) as input evidence. Experiments conducted with synthetic and real images, involving rigid and deformable objects, show the robustness of the proposed methods with respect to geometrical transformations, heavy clutter and substantial occlusion

Citrus Fruit Feature Extraction using Colpromatix Color Code Model

Classification of citrus fruit more precisely and economically under natural illumination circumstances. The aim of this paper was to develop a robust and feature extraction techniques to discover citrus fruit features with different dimensions and under different illumination conditions. To identify object residing in image, the image has to be described or represented by certain features. In this paper, proposed a citrus fruit feature extraction process for deriving the classification. The proposed system present two tasks namely, 1) Image pre-processing: it is carried out using Hybrid Noise filter to remove the noise; ii) Citrus fruit features extraction: Feature extraction using new Colpromatix color space model, Size, Texture, Shape, and Coarseness. The Image Shape is an important visual feature of an image. Difference features representation and description techniques are discuss in this review paper. Feature extraction techniques play an important role in systems for object recognition, matching, extracting, and analysis. It also presents comparison between various techniques

Learning Physically Realizable Skills for Online Packing of General 3D Shapes

We study the problem of learning online packing skills for irregular 3D shapes, which is arguably the most challenging setting of bin packing problems. The goal is to consecutively move a sequence of 3D objects with arbitrary shapes into a designated container with only partial observations of the object sequence. Meanwhile, we take physical realizability into account, involving physics dynamics and constraints of a placement. The packing policy should understand the 3D geometry of the object to be packed and make effective decisions to accommodate it in the container in a physically realizable way. We propose a Reinforcement Learning (RL) pipeline to learn the policy. The complex irregular geometry and imperfect object placement together lead to huge solution space. Direct training in such space is prohibitively data intensive. We instead propose a theoretically-provable method for candidate action generation to reduce the action space of RL and the learning burden. A parameterized policy is then learned to select the best placement from the candidates. Equipped with an efficient method of asynchronous RL acceleration and a data preparation process of simulation-ready training sequences, a mature packing policy can be trained in a physics-based environment within 48 hours. Through extensive evaluation on a variety of real-life shape datasets and comparisons with state-of-the-art baselines, we demonstrate that our method outperforms the best-performing baseline on all datasets by at least 12.8% in terms of packing utility.Comment: ACM Transactions on Graphics (TOG

Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review

Industry 4.0 has become a crucial part in the majority of processes, components, and related modelling, as well as predictive tools that allow a more efficient, automated and sustainable approach to industry. The availability of large quantities of data, and the advances in IoT, AI, and data-driven frameworks, have led to an enhanced data gathering, assessment, and extraction of actionable information, resulting in a better decision-making process. Product picking and its subsequent packing is an important area, and has drawn increasing attention for the research community. However, depending of the context, some of the related approaches tend to be either highly mathematical, or applied to a specific context. This article aims to provide a survey on the main methods, techniques, and frameworks relevant to product packing and to highlight the main properties and features that should be further investigated to ensure a more efficient and optimised approach

Non-destructive technologies for fruit and vegetable size determination - a review

Here, we review different methods for non-destructive horticultural produce size determination, focusing on electronic technologies capable of measuring fruit volume. The usefulness of produce size estimation is justified and a comprehensive classification system of the existing electronic techniques to determine dimensional size is proposed. The different systems identified are compared in terms of their versatility, precision and throughput. There is general agreement in considering that online measurement of axes, perimeter and projected area has now been achieved. Nevertheless, rapid and accurate volume determination of irregular-shaped produce, as needed for density sorting, has only become available in the past few years. An important application of density measurement is soluble solids content (SSC) sorting. If the range of SSC in the batch is narrow and a large number of classes are desired, accurate volume determination becomes important. A good alternative for fruit three-dimensional surface reconstruction, from which volume and surface area can be computed, is the combination of height profiles from a range sensor with a two-dimensional object image boundary from a solid-state camera (brightness image) or from the range sensor itself (intensity image). However, one of the most promising technologies in this field is 3-D multispectral scanning, which combines multispectral data with 3-D surface reconstructio