An Effective Defect Detection and Warning Prioritization Approach for Resource Leaks

Resource leaks in Android apps are pervasive. They can cause serious performance degradation and system crashes. In recent years, many resource leak detection techniques have been proposed to help Android developers correctly manage system resources. Yet, there exist no common databases of real-world bugs for effectively comparing such techniques to understand their strengths and limitations. This paper describes our effort towards constructing such a bug database named DroidLeaks. To extract real resource leak bugs, we mined 124,215 code revisions of 34 popular open-source Android apps. After automated filtering and manual validation, we successfully found 292 fixed resource leak bugs, which cover a diverse set of resource classes, from 32 analyzed apps. To understand these bugs, we conducted an empirical study, which revealed the characteristics of resource leaks in Android apps and common patterns of resource management mistakes made by developers. To further demonstrate the usefulness of our work, we evaluated eight resource leak detectors from both academia and industry on DroidLeaks and performed a detailed analysis of their performance. We release DroidLeaks for public access to support future research.

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.

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

In the industrial production of expanded thermoplastic polyurethane (E-TPU) midsoles, the surface defects still rely on manual inspection at present, and the eligibility criteria are uneven. Therefore, this paper proposes an E-TPU midsole surface defect detection method based on machine vision to achieve automatic detection and defect classification. The proposed method is divided into three parts: image preprocessing, block defect detection, and linear defect detection. Image preprocessing uses RGB three channel self-inspection to identify scorch and color pollution. Block defect detection uses superpixel segmentation and background prior mining to determine holes, impurities, and dirt. Linear defect detection uses Gabor filter and Hough transform to detect indentation and convex marks. After image preprocessing, block defect detection and linear defect detection are simultaneously performed by parallel computing. The false positive rate (FPR) of the proposed method in this paper is 8.3%, the false negatives rate (FNR) of the hole is 4.7%, the FNR of indentation is 2.1%, and the running time does not exceed 1.6 s. The test results show that this method can quickly and accurately detect various defects in the E-TPU midsole.

Capacitive touch panels (CTPs), as a medium of information interactions, have become essential parts in many consumer electronics. However, current methods such as image edge matching and frequency notch filter cannot suit the defect detection for the new-type complex CTP patterns, which have neither basic primitives nor periodicity. For solving the issues, we proposed a nonnegative matrix factorization (NMF)-based large-size image registration method, and combined it with image tolerance models to detect defects in such CTP patterns. The NMF-based image registration method can fast extract each CTP from a large image. And then, any three of registered images are selected as reference images, which are further processed by threshold processing and simple mathematical morphological operation to obtain tolerance models. Afterward, we can use the tolerance models to obtain a nondefective template. In the normal inspection stage, the defects in CTP patterns can be identified as long as comparing the tolerance models of the template and sensed images. The experimental results show that the proposed method can efficiently and accurately detect various types of defects in CTP patterns. Moreover, the detection results are robust under different illuminations. Therefore, this algorithm can be reliably applied in actual inspection of such new-type CTP patterns.

Replacing time-consuming and costly manual inspections on production lines with efficient and accurate defect detection algorithms for Printed Circuit Boards (PCBs) remains a significant challenge. Current PCB defect detection methods are typically optimized using existing models such as YOLO and Faster R-CNN to enhance detection accuracy. In this study, we analyse a PCB defect dataset characterized by small targets and a concentrated size distribution. SEPDNet (Simple and Effective PCB Defect Detection Network) is designed for the characteristics of the dataset, only one detection head is used, which reduces the number of parameters and improves the detection performance at the same time. SEPDNet uses RepConv (Re-parameterizable Convolution) to improve the backbone representation ability, and FPN (Feature Pyramid Network) is used in the neck part to simplify the model. SEPDNet has fewer than 30% of the parameters of YOLOv9u-s, yet achieves an improvement of 0.025 in the F1 score, 2.7% in mAP50, and 3.8% in mAP50:95 compared to YOLOv9u-s. We propose the method of designing the model according to the characteristics of the dataset. Our experiments show that customizing the model design according to dataset characteristics can achieve strong performance with a simplified structure and fewer parameters.

Integral images-based approach for fabric defect detection

This research aims to develop smart defect detection and sortation for corn with the use of image processing. This project helps to speed up manual inspection to improve productivity and reduce the time consumed by farmers in sorting. The defect detection process is done through image processing using an open computer vision library and Python. The corn is manually placed in the roller conveyor, passing under a camera acquiring the real-time image. The sorting process eliminates the damaged corn from those that were in good condition. The system acquires real-time image data from a camera feed to a computer for analyzing purposes. Images were scanned as the corn ear was traveling through the conveyor. A program that effectively analyzes acquired corn images' required features was developed in Python using the Open Computer Vision (OCV) library. A conveyor that comes with a built-in corn sortation mechanism was controlled by Raspberry Pi that directs the corn to its desired group. On the evaluation of the accuracy of the system, a series of trials were conducted. The result of the evaluation yielded a 92% success rate in terms of defect detection and sortation. Revisions were made after the initial testing of the project. Problems and its causes were identified to improve the performance of the whole system.

To ensure the safety of oil and gas pipelines, the key point is the effective detection of defects, and to make an accurate assessment of their damage. The technology of alternating current field measurement (ACFM) is one kind of non-contact measurement technologies, especially for the detection of surface defects such as cracks. And its test results would be not affected by the media of the pipeline or the coating of inner surface like paint or wax layer. However, the conventional research on ACFM does not point at inner-detection of pipeline. The detection sets based on ACFM have a resolution that is still low. And it has not been applied to the using of long-distance oil and gas pipeline. Based on technical characteristics of ACFM, a practical inner-detection device has been designed, in view of the current working condition for in-service pipeline. The detection probe uses pairs of coils in parallel arrange as the excitation coil. The mathematical model is used to derive the electromagnetic field distribution. Simulations in the computer and experiments on the physical pipeline are carried out to prove the effectiveness of this probe and to analyze the relevant factors that affects the detection. The device has the ability of quantitative detection of defects in the pipeline, and effectively achieves high detection sensitivity as the minimal identifiable defect has a dimension with a 1mm in depth and 3mm in length.

This paper proposes a novel template-based correction (TC) method for the defect detection on images with periodic structures. In this method, a fabric image is segmented into lattices according to variation regularity, and correction is applied to reduce the effect of misalignment among lattices. Also, defect-free lattices are chosen for establishing an average template as a uniform reference. Furthermore, the defect detection procedure is composed of two steps, namely, defective lattices locating and defect shape outlining. Defective lattices locating is based on classification for defect-free and defective patterns, which involves an improved E-V method with template-based correction and centralized processing, while defect shape outlining provides pixel-level results by threshold segmentation. In this paper we also present some experiments on fabric defect detection. Experimental results show that the proposed method is effective.

Laser ultrasonic produces frequencies in the MHz range, enabling high accuracy and a strong ability to detect rail surface defects. This paper mainly studied on the simulation of detecting surface-connected rail defects on 60 kg rails with laser ultrasonic, established the finite element model of laser-excited ultrasonic Rayleigh wave, carried out the simulation, and verified the effectiveness of the technology through experiments. To solve the problem that laser ultrasonic is insensitive to the width of defects in actual detection, and unable to make quantitative detection of defects, this paper established a new model on the basis of improving the original model that has been verified, exciting ultrasonic at the two sides at the same time of a rail with two staggered beams of laser separately to detect irregular scratch defects on rail surface, and two groups of signal data were received through two probes. Each group of data can present the half-profile information of defects, and further form two detection images of the defect. At last, the two detection images were combined into a complete image through image processing. The results of the experiment indicate that the technology studied offers a new method for the effective quantitative detection of surface-connected defects on rail.

Abstract: Fabric production companies place great emphasis on quality control, recognizing its crucial role in their operations. Failure to identify defects in fabrics exposes these companies to the risk of financial losses and damage to their reputation due to the sale of flawed products. Typically, traditional inspection systems exhibit an accuracy range of 60-75%. This paper proposes a solution to reduce costs by introducing a fast and automated defect detection system, complemented by human operator decision-making. The system employs a specialized Convolutional Neural Network (CNN) for defect detection. The training process involved the utilization of over 50 defect types to achieve a well-generalized system. Moreover, given that undetected defects (False Negatives - FNs) are more costly to the company than erroneously classifying a non-defective fabric as defective (false positives), FN reduction methods were incorporated into the proposed system. During testing, the automatic mode of the system achieved an average accuracy of 75%.

Motivation: Certain studies use short-TE MRS to estimate metabolites such as gamma-aminobutyric acid. The findings of such studies hinge on the correlation of this technique to the gold-standard edited MRS technique. Goal(s): We investigate how the false negative rate and false positive rate of studies vary as a function of correlation between short-TE and MEGA-edited metabolite estimates. Approach: Simulated data was used to numerically estimate the false positive and false negative rates, using parameters obtained from the literature. Results: We show that these studies can have largely inflated false positive and false negative rates as compared to their nominally designed values. Impact: The false negative and false positive rate can be markedly inflated when using short-TE MRS to estimate metabolites such as gamma-aminobutyric acid. This calls for increased caution in interpreting results, and the accommodation of these effects when designing statistical analyses.

BAND: BAgging noise detectors with application to semiconductor wafer denoising

Cascaded detection method for surface defects of lead frame based on high-resolution detection images