The PASCAL Visual Object Classes Challenge

Texture features for image classification and retrieval.

Autonomous mobile robots are becoming more prominent in recent time because of their relevance and applications to the world today. Their ability to navigate in an environment without a need for physical or electro-mechanical guidance devices has made it more promising and useful. The use of autonomous mobile robots is emerging in different sectors such as companies, industries, hospital, institutions, agriculture and homes to improve services and daily activities. Due to technology advancement, the demand for mobile robot has increased due to the task they perform and services they render such as carrying heavy objects, monitoring, search and rescue missions, etc. Various studies have been carried out by researchers on the importance of mobile robot, its applications and challenges. This survey paper unravels the current literatures, the challenges mobile robot is being faced with. A comprehensive study on devices/sensors and prevalent sensor fusion techniques developed for tackling issues like localization, estimation and navigation in mobile robot are presented as well in which they are organised according to relevance, strengths and weaknesses. The study therefore gives good direction for further investigation on developing methods to deal with the discrepancies faced with autonomous mobile robot.

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A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods

Machine vision intelligence for product defect inspection based on deep learning and Hough transform

Machine Learning (ML) is a branch of artificial intelligence that studies algorithms able to learn autonomously, directly from the input data. Over the last decade, ML techniques have made a huge leap forward, as demonstrated by Deep Learning (DL) algorithms implemented by autonomous driving cars, or by electronic strategy games. Hence, researchers have started to consider ML also for applications within the industrial field, and many works indicate ML as one the main enablers to evolve a traditional manufacturing system up to the Industry 4.0 level. Nonetheless, industrial applications are still few and limited to a small cluster of international companies. This paper deals with these topics, intending to clarify the real potentialities, as well as potential flaws, of ML algorithms applied to operation management. A comprehensive review is presented and organized in a way that should facilitate the orientation of practitioners in this field. To this aim, papers from 2000 to date are categorized in terms of the applied algorithm and application domain, and a keyword analysis is also performed, to details the most promising topics in the field. What emerges is a consistent upward trend in the number of publications, with a spike of interest for unsupervised and especially deep learning techniques, which recorded a very high number of publications in the last five years. Concerning trends, along with consolidated research areas, recent topics that are growing in popularity were also discovered. Among these, the main ones are production planning and control and defect analysis, thus suggesting that in the years to come ML will become pervasive in many fields of operation management.

Machine Learning for industrial applications: A comprehensive literature review

Helmet wearing is very important to the safety of workers at construction sites and factories. How to warn/identify/certify workers “whether or not the helmet is worn” is often a difficult point for enterprises to monitor. Based on the YOLO V3 full-regression deep neural network architecture, this paper utilizes the advantage of Densenet in model parameters and technical cost to replace the backbone of the YOLO V3 network for feature extraction, thus forming the so-called YOLO-Densebackbone convolutional neural network. The test results show that the improved model can effectively deal with situations that the helmet is stained, partially occluded, or there are many targets with a low image resolution. In the test set, compared with the traditional YOLO V3, the improved algorithm detection accuracy increased by 2.44% with the same detection rate. The establishment of this model has important practical significance for improving helmet detection and ensuring safe construction.

Helmet Detection Based On Improved YOLO V3 Deep Model

A robust vision inspection system for detecting surface defects of film capacitors

Recently, most state-of-the-art object detection systems adopt anchor box mechanism to simplify the detection model. Neural networks only need to regress the mapping relations from anchor boxes to ground truth boxes, then prediction boxes can be calculated using information from outputs of networks and default anchor boxes. However, when the problem becomes complex, the number of default anchor boxes will increase with large risk of over-fitting during training. In this paper, we adopt an adaptive anchor box mechanism that one anchor box can cover more ground truth boxes. So networks only need a few adoptive anchor boxes to solve the same problem and the model will be more robust. The sizes of adaptive anchor boxes will be adjusted automatically according to the depth collected by a Time of Flight (TOF) camera. The network adjusts the aspect ratios of anchor boxes to get final prediction boxes. The experimental results demonstrate that the proposed method can get more accurate detection results. Specifically, using the proposed adaptive anchor box mechanism, the Mean Average Precision (mAP) of YOLO-v2 and YOLO-v3 networks increases obviously on open public datasets and our self-built battery image dataset. Moreover, the visual results of prediction comparisons also illustrate that the proposed adaptive anchor box mechanism can achieve better performance than original anchor box mechanism.

Adaptive anchor box mechanism to improve the accuracy in the object detection system

In order to improve the effectiveness of aperture detection and enhance the competitiveness of enterprise, an automatic inspection system for detecting the aperture of Led cup is developed in the paper. The proposed system can achieve detecting the aperture and separating the unqualified Led cups robustly. Specifically, efficient approaches based on three-point circle fitting and convolutional neural network (CNN) are proposed to achieve automatic aperture detection. Then, a novel control unit is designed to separate the unqualified Led cups using the gas claw and air cylinder. Experimental results demonstrate that the detecting accuracy of the developed system can well meet the requirements of manufacturing enterprise. Moreover, the proposed system can greatly save time and labor costs for enterprises. In addition, with this system we can efficiently construct a vision big data of LED cups. Using such a vision big data, problems of the production line can be timely discovered, and the production quality will be greatly improved.

Yuxiang Yang

Papers

Helmet Detection Based On Improved YOLO V3 Deep Model

A robust vision inspection system for detecting surface defects of film capacitors

Adaptive anchor box mechanism to improve the accuracy in the object detection system

An automatic aperture detection system for LED cup based on machine vision

Depth map super-resolution using stereo-vision-assisted model