Smart Defect Detection and Sortation through Image Processing for Corn

The design and development of image processing units (IPUs) has traditionally involved trade-offs between cost, real-time properties, portability, and ease of programming. A standard PC can be turned into an IPU relatively easily with the help of readily available computer vision libraries, but the end result will not be portable, and may be costly. Similarly, one can use field programmable gate arrays (FPGAs) as the base for an IPU, but they are expensive and require hardware-level programming. Finally, general purpose embedded hardware tends to be under-powered and difficult to develop for due to poor support for running advanced software. In recent years a new option has surfaced: single-board computers (SBCs). These generally inexpensive embedded devices would be attractive as a platform on which to develop IPUs due to their inherent portability and good compatibility with existing computer vision (CV) software. However, whether their performance is sufficient for real-time image processing has thus far remained an open question. Most SBCs (especially the ultra-low-cost ones which we target) do not offer CUDA/OpenCL support which makes it difficult to port GPU-based CV applications. In order to utilize the full power of the SBCs, their GPUs need to be used. In our attempts at doing this, we have observed that the CV algorithms which an IPU uses have to be re-designed according to the OpenGL support available on these devices. This work presents a framework where a selection of CV algorithms have been designed in a way that they optimize performance on SBCs while still maintaining portability across devices which offer OpenGL ES 2.0 support. Furthermore, this paper demonstrates an IPU based on a representative SBC (namely the Raspberry Pi) along with two CV applications backed by it. The robustness of the applications as well as the performance of the IPU are evaluated to show that SPCs can be used to build IPUs capable of producing accurate data in real time. This opens the possibilities of large scale economically deployment of vision system especially in remote and barren lands. Finally, the software developed as a part of this work has been released open source.

In this system, it helps to convert the sign language to voice with hand gestures understanding and capture the motion of hands. Many sign languages are natural languages, but they are differing in construction from oral languages used in proximity to them, and are employed mainly by deaf people in order to communicate. It is based on a Raspberry Pi with a camera module and is programmed in Python with the Open-Source Computer Vision (Open CV) library as a backend. A built-in image processing algorithm on the Raspberry Pi is named gesture that tracks an object (a finger) with features pulled out. The main purpose of a gesture recognition system is to establish a connection between a human and a computer control system. Camera is used in this system and it captures the various gestures of hands. Various algorithms are taking place in processing of image. First preprocessing of the image takes place. Finally, the sign is identified by using Tensor flow algorithm and the outcome result as a voice using TTS algorithm. Open CV Python is implemented in this system. Various libraries are used in this system.

In addition to causing damage to vehicles, road defects are one of the main causes of vehicle accidents which lead to loss of human lives. Many methods of detecting defects have been introduced over the years to reduce the consequences of these defects. One of these methods is image processing. Use of image and video processing has many applications in medicine, science, agriculture, and defect detection in structures. It has been used for defect detection on roads because timely detection and analysis of defect is very important for road serviceability and safety of the people. Detection of a defect by image processing broadly follows some of the basic steps which include feature extraction, edge detection, morphological operators, and training of data. Different approaches are used for various kinds of defect detection and analysis which have replaced the manual inspection method of roads saving time and resources. This chapter discusses the basic steps involved in defect detection using image processing along with existing systems that use machine learning and artificial intelligence for the detection of defects from a distance. To write this chapter, papers on the topic of image and computer vision-based defect detection systems have been consulted.

Purpose. The main goal of this study is to review and systematize the materials and defects of the cylinder block and cylinder liners of internal combustion engines (ICE), as well as to analyze the issues and current trends in defect detection during repair. Methodology. The main problems currently associated with the defect detection of cylinder blocks and liners of ICEs include the following: the rapid growth of motorization, which leads to an increase in the number of cylinder blocks and liners requiring defect inspection; the use of manual labor, which makes the defect detection process labor-intensive and costly; the need for classification of defect types and their automatic recognition; the consideration of operating conditions influencing defect development; the necessity of implementing new defect detection processes; the need for quantitative wear assessment and prediction of component residual life; the adaptation of inspection methods and evaluation criteria to new materials. The study employs the following research methods: theoretical analysis (classification, systematization), review and synthesis of known scientific results, and empirical methods (comparison, observation). Findings. The authors conducted a review and systematization of structural materials and the main defects of cylinder blocks and liners of ICEs. The main issues in modern defect detection were identified as follows: insufficient process automation, low accuracy of visual and instrumental inspection methods, limited adaptability of existing inspection techniques to new materials, and the need for quantitative evaluation of component residual life. The development trends in defect detection systems are defined as the transition to automated and computerized inspection technologies, the creation of databases of typical defects, and the development of algorithms for predicting the residual life of components. Originality. The scientific novelty of this work lies in a comprehensive approach to analyzing structural materials, defects, and modern trends in defect detection of cylinder blocks and liners of ICEs. Materials used in component manufacturing have been systematized, a defect classification has been proposed, and the factors influencing wear intensity and defect formation have been identified. The trends in the development of defect detection systems have been established toward automation, increased sensitivity, and higher inspection accuracy. Practical value. The conducted analysis contributes to improving the efficiency of defect detection and repair processes of cylinder blocks and liners of ICEs, as well as to the development of informational databases of typical defects and the conditions under which they occur.

This research presents a fuzzy controller technique in navigation with obstacle avoidance for a general purpose mobile robot in a given global environment with image processing technique using Open Source Computer Vision (OpenCV) library on Visual C++. Fuzzy Logic is used to control the navigation of the robot towards the goal while avoiding obstacles along the way by changing its direction of movement. The positions of the mobile robot, obstacle and the destination are taken into consideration and an overhead camera (above the robot’s environment) is used to gather these necessary information. The images captured are processed using different techniques to get the desired positions and is directly integrated with the fuzzy controller making the algorithm more efficient compared to other vision-guided navigation techniques.

Defect detection method for rail surface based on line-structured light

Stromatoporoid biometrics using image analysis software: A first order approach

Purpose. To compare the penetration of riboflavin using a microscope-integrated real time spectral domain optical coherence tomography (ZEISS OPMI LUMERA 700 and ZEISS RESCAN 700) in keratoconus patients undergoing accelerated collagen crosslinking (ACXL) between epithelium on (epi-on) and epithelium off (epi-off). Methods. Intraoperative images were obtained during each of the procedures. Seven keratoconus patients underwent epi-on ACXL and four underwent epi-off ACXL. A software tool was developed using Microsoft.NET and Open Computer Vision (OpenCV) libraries for image analysis. Pre- and postprocedure images were analyzed for changes in the corneal hyperreflectance pattern as a measure of the depth of riboflavin penetration. Results. The mean corneal hyperreflectance in the epi-on group was 12.97 ± 1.49 gray scale units (GSU) before instillation of riboflavin and 14.46 ± 2.09 GSU after AXCL (P = 0.019) while in the epi-off group it was 11.43 ± 2.68 GSU and 16.98 ± 8.49 GSU, respectively (P = 0.002). The average depth of the band of hyperreflectance in the epi-on group was 149.39 ± 15.63 microns and in the epi-off group it was 191.04 ± 32.18 microns. Conclusion. This novel in vivo, real time imaging study demonstrates riboflavin penetration during epi-on and epi-off ACXL.

Failing to release unneeded system resources such as I/O streams can result in resource leaks, which can lead to performance degradation and system crashes. Existing resource-leak detectors are usually based on predefined defect patterns to detect resource leaks in software. However, they typically report too many false positives and negatives, and also lack effective warning prioritization. Our empirical investigation shows that, their predefined defect patterns are not precise enough, and moreover, their used defect detection processes are not suitable enough for the defect patterns. In our approach, we introduce a novel Expressive Defect Pattern Specification Notation (EDPSN). With EDPSN, a resource-leak defect pattern can be defined more precisely by specifying conditional method calls and more expressively by including guiding information for the defect detection and warning prioritization process, such as the characteristics of its preferred defect detection process and the effective prioritization impact factors for its related warnings. Based on the EDPSN-based defect pattern, our approach tries to flexibly tune out a suitable defect detection and warning prioritization process. Through evaluations on three real-world projects (Eclipse-3.0.1, JBoss-3.0.6, and Weka-3.6.4), we show that our approach achieves high average precision (96%) and recall (74%), 26% and 49% higher than existing approaches, respectively.

Factors to Consider in Augmentation Urethroplasty with Oral Mucosa Graft or Penile Skin Flap for Anterior Urethral Stricture: A Systematic Review and Meta-analysis

Integral images-based approach for fabric defect detection

Penile Surgery for ED, Urologists and Sexologists

Fiber reinforced polymer (FRP) bridge decks are currently being explored as an alternative to the traditional reinforced concrete bridge decks because of their higher strength-to-weight ratio and better corrosion resistance properties. However, the long-term degradation behavior of FRP bridge decks is not very well understood. For ensuring continued structural integrity of these FRP decks, it is important to employ periodic field monitoring technique such as infrared thermography. The technique allows rapid data collection and interpretation in the field. Also, recent advances in digital infrared imaging has opened the door for using more advanced image enhancement techniques, which can lead to better sensitivity and reliability in the use of the infrared technique. This paper presents an example of the use of digital infrared thermography on a laboratory specimen of FRP bridge deck. The specimen was cast with embedded subsurface defects (debonds) which were successfully located using the infrared technique. The paper also demonstrates the use of digital signal processing methodology to aid in the defect detection process. The results show that infrared technique can be a useful tool for defect detection in field composite decks, and also for quality control during pultrusion of new decks (in factories) and during field construction.

During the industrial processing of materials for the manufacture of new products, surface defects can quickly occur. In order to achieve high quality without a long time delay, it makes sense to inspect the work pieces so that defective work pieces can be sorted out right at the beginning of the process. At the same time, the evaluation unit should come close the perception of the human eye regarding detection of defects in surfaces. Such defects often manifest themselves by a deviation of the existing structure. The only restriction should be that only matt surfaces should be considered here. Therefore in this work, different classification and image processing algorithms are applied to surface data to identify possible surface damages. For this purpose, the Gabor filter and the FST (Fused Structure and Texture) features generated with it, as well as the salience metric are used on the image processing side. On the classification side, however, deep neural networks, Convolutional Neural Networks (CNN), and autoencoders are used to make a decision. A distinction is also made between training using class labels and without. It turns out later that the salience metric are best performed by CNN. On the other hand, if there is no labeled training data available, a novelty classification can easily be achieved by using autoencoders as well as the salience metric and some filters.

This paper presents an automatic visual inspection of exterior surface defects of oil tanks using unmanned aerial vehicles (UAVs) and image processing with two cascading fuzzy logic algorithms. Corrosion is one of the defects that has a serious effect on the safety of the surface of oil and gas tanks. At present, human inspection, and climbing robots inspection are the dominant approach for rust detection in oil and gas tanks. However, there are many shortcomings to this approach, such as taking longer, high cost, and covering less surface area inspection of the tank. The purpose of this research is to detect the rust in oil tanks by localizing visual inspection technology using UAVs, as well as to develop algorithms to distinguish between defects and noise. The study focuses on two basic aspects of oil tank inspection through the images captured by the UAV, namely, the detection of defects and the distinction between defects and noise. For the former, an image processing algorithm was developed to improve or remove noise, adjust the brightness of the captured image, and extract features to identify defects in oil tanks. Meanwhile, for the latter aspect, a cascading fuzzy logic algorithm and threshold algorithm were developed to distinguish between defects and noise levels and reduce their impact through three stages of processing: The first stage of fuzzy logic aims to distinguish between defects and low noise generated by the appearance of objects on the surface of the tank, such as trees or stairs, and reduce their impact. The second stage aims to distinguish between defects and medium noise generated by shadows or the presence of small objects on the surface of the tank and reduce their impact. The third stage of the thresholding algorithm aims to distinguish between defects and high noise generated by sedimentation on the surface of the tank and reduce its impact. The samples were classified based on the output of the third stage of the threshold process into defective or non-defective samples. The proposed algorithms were tested on 180 samples and the results show its superiority in the inspection and detection of defects with an accuracy of 83%.