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Impact of image preprocessing and Crack Type Distribution on YOLOv8-Based Road Crack Detection

SENSORS 2025年第7期25卷 2180-2180页

作者： Fan, Luxin Tang, Saihong Ariffin, Mohd Khairol Anuar b. Mohd Ismail, Mohd Idris Shah Wang, Xinming Univ Putra Malaysia UPM Fac Engn Serdang 43400 Selangor Malaysia

Road crack detection is crucial for ensuring pavement safety and optimizing maintenance strategies. This study investigated the impact of image preprocessing methods and dataset balance on the performance of YOLOv8s-based crack detection. Four datasets (CFD, Crack500, CrackTree200, and CrackVariety) were evaluated using three image formats: RGB, grayscale (five conversion methods), and binarized images. The experimental results indicate that RGB images consistently achieved the highest detection accuracy, confirming that preserving color-based contrast and texture information benefits YOLOv8's feature extraction. Grayscale conversion showed dataset-dependent variations, with different methods performing best on different datasets, while binarization generally degraded detection accuracy, except in the balanced CrackVariety dataset. Furthermore, this study highlights that dataset balance significantly impacts model performance, as imbalanced datasets (CFD, Crack500, CrackTree200) led to biased predictions favoring dominant crack classes. In contrast, CrackVariety's balanced distribution resulted in more stable and generalized detection. These findings suggest that dataset balance has a greater influence on detection accuracy than preprocessing methods. Future research should focus on data augmentation and resampling strategies to mitigate class imbalance, as well as explore multi-modal fusion approaches for further performance enhancements.

关键词： YOLOv8 crack detection image preprocessing deep learning dataset balancing pavement inspection computer vision

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DICOM LUT is a Key Step in Medical image preprocessing Towards AI Generalizability

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JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2025年 1-9页

作者： Dapamede, Theo Li, Frank Khosravi, Bardia Purkayastha, Saptarshi Trivedi, Hari Gichoya, Judy Emory Univ Dept Radiol Atlanta GA 30322 USA Mayo Clin Dept Radiol Rochester MN USA Indiana Univ Luddy Sch Informat Comp & Engn Indianapolis IN USA

image pre-processing has significant impact on performance of deep learning models in medicine;yet, there is no standardized method for DICOM pre-processing. In this study, we investigate the impact of two commonly used image preprocessing techniques, histogram equalization (HE) and values-of-interest look-up-table (VOI-LUT) transformations on the performance deep learning classifiers for chest X-rays (CXR). We generated two baseline datasets (raw pixel and standard DICOM processed) from our internal CXR dataset and then enhanced both with HE to create four distinct datasets. Four independent deep learning models for diagnosis of pneumothorax were trained and evaluated on two external datasets. Results reveal that HE enhancement significantly affects model performance, particularly in terms of generalizability. Models trained solely on HE-enhanced datasets exhibit poorer performance on external validation sets, suggesting potential overfitting and information loss. These models also exhibit shortcut learning, relying on spurious correlations in the training data for their prediction. This study highlights the importance of machine learning practitioners being aware of preprocessing techniques applied to datasets and their potential impacts on model performance, as well as need for including preprocessing information when sharing datasets. Additionally, this research underscores the necessity of using pixel values closer to clinical standards during dataset curation to improve model robustness and mitigate the risk of information loss.

关键词： Dataset curation image preprocessing Chest X-ray VOI Histogram equalization

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Transfer learning and single-polarized SAR image preprocessing for oil spill detection

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ISPRS Open Journal of Photogrammetry and Remote Sensing 2025年 15卷

作者： Kussul, Nataliia Salii, Yevhenii Kuzin, Volodymyr Yailymov, Bohdan Shelestov, Andrii National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Department of Mathematical Modelling and Data Analysis Kyiv Ukraine Space Research Institute NAS Ukraine and SSA Ukraine Department of Space Information Technologies and Systems Kyiv Ukraine University of Maryland Department of Geographical Sciences College Park United States

This study addresses the challenge of oil spill detection using Synthetic Aperture Radar (SAR) satellite imagery, employing deep learning techniques to improve accuracy and efficiency. We investigated the effectiveness of various neural network architectures and encoders for this task, focusing on scenarios with limited training data. The research problem centered on enhancing feature extraction from single-channel SAR data to improve oil spill detection performance. Our methodology involved developing a novel preprocessing pipeline that converts single-channel SAR data into a three-channel RGB representation. The preprocessing technique normalizes SAR intensity values and encodes extracted features into RGB channels. Through an experiment, we have shown that a combination of the LinkNet with an EfficientNet-B4 is superior to pairs of other well-known architectures and encoders. Quantitative evaluation revealed a significant improvement in F1-score of 0.064 compared to traditional dB-scale preprocessing methods. Qualitative assessment on independent SAR scenes from the Mediterranean Sea demonstrated better detection capabilities, albeit with increased sensitivity to look-alike. We conclude that our proposed preprocessing technique shows promise for enhancing automatic oil spill segmentation from SAR imagery. The study contributes to advancing oil spill detection methods, with potential implications for environmental monitoring and marine ecosystem protection. © 2024 The Authors

关键词： Deep learning image preprocessing Oil spill detection Synthetic aperture radar (SAR) Transfer learning

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image preprocessing with a parallel optoelectronic processor

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COMPUTERS & ELECTRICAL ENGINEERING 2015年 46卷 554-565页

作者： Rudi, Ali Gholami Jalili, Saeed Tarbiat Modares Univ Dept Elect & Comp Engn Tehran Iran

In this paper we use and extend a parallel optoelectronic processor for image preprocessing and implement software tools for testing and evaluating the presented algorithms. After briefly introducing the processor and showing how images can be stored in it, we adapt a number of local image preprocessing algorithms for smoothing, edge detection, and corner detection, such that they can be executed on the processor in parallel. These algorithms are performed on all pixels of the input image in parallel and, as a result, in steps independent of its dimensions. We also develop a compiler and a simulator for evaluating and verifying the correctness of our implementations. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Parallel image processing Parallel optical processing High-performance computing image preprocessing Parallel processing

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image preprocessing for rotation-invariant pattern recognition in the presence of signal-dependent noise

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APPLIED OPTICS 1996年第11期35卷 1879-1893页

作者： Terrillon, JC []The author is a fellow of the Science and Technology Agency of Japan with the Kansai Advanced Research Center Communications Research Laboratory Ministry of Posts and Telecommunications 588-2 Iwaoka Nishi-ku Kobe 651-24 Japan.

I propose a new method that ensures efficient rotation-invariant pattern recognition in the presence of signal-dependent noise by combining the application of rotation-invariant correlation filters with preprocessing of the noisy input images. The preprocessing uses local suboptimal estimators derived from estimation theory and implies an a priori knowledge of a model describing the noise source. The image noise sources considered are speckle and film-grain noise. Four different metrics are used to analyze the correlation performance of the circular-harmonic filter, the phase-only circular-harmonic filter, and the binary phase-only circular-harmonic filter, with and without a preprocessing. Computer simulations show that signal-dependent noise can seriously degrade the performance of the phase-only circular-harmonic filter and the binary phase-only circular-harmonic filter. The most severe indication of correlation-performance degradation is the occurrence of false alarms in 15% to 20% of noise realizations of the correlation. preprocessing increases the correlation-peak signal-to-noise ratio significantly and reduces the false-alarm probability by one to two orders of magnitude. (C) 1996 Optical Society of America

关键词： signal-dependent noise pattern recognition correlation filtering image preprocessing

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image preprocessing method based on local approximation gradient with application to face recognition

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PATTERN ANALYSIS AND APPLICATIONS 2017年第1期20卷 101-112页

作者： Li, Zhaokui Wang, Yan Fan, Chunlong He, Jinrong Shenyang Aerosp Univ Sch Comp Daoyi South 37th St Shenyang 110136 Liaoning Peoples R China Northwest A&F Univ Coll Informat Engn Yangling 712100 Shanxi Peoples R China

In order to obtain more robust face recognition results, the paper proposes an image preprocessing method based on local approximation gradient (LAG). The traditional gradient is only calculated along 0A degrees and 90A degrees;however, there exist many other directional gradients in an image block. To consider more directional gradients, we introduce a novel LAG operator. The LAG operator is actually calculated by integrating more directional gradients. Because of considering more directional gradients, LAG captures more edge information for each pixel of an image and finally generates an LAG image, which achieves a more robust image dissimilarity between images. An LAG image is normalized into an augmented feature vector using the "z-score" method. The dimensionality of the augmented feature vector is reduced by linear discriminant analysis to yield a low-dimensional feature vector. Experimental results show that the proposed method achieves more robust results in comparison with state-of-the-art methods in AR, Extended Yale B and CMU PIE face database.

关键词： Local approximate gradient image preprocessing Linear discriminant analysis Robust dissimilarity Face recognition

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image preprocessing of Iris Recognition 3

Image Preprocessing of Iris Recognition

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3rd IEEE International Conference on Integrated Circuits and Microsystems (ICICM)

作者： Sun, Yangqing Hua, Yuanyuan Nanjing Inst Ind Technol Sch Comp & Software Nanjing Jiangsu Peoples R China Chinese Acad Sci Purple Mt Observ CAS Key Lab Space Object & Debris Observat Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538683118

The aim of this paper is to propose the methods for image preprocessing of iris recognition including image enhancement and boundary detection. Iris recognition has been widely considered as one of the most dependable identification method. However, the iris systems are still not widespread due to many factors, for example, the production cost, the processing time and the recognition rate. The problems of production cost and the processing time will be resolved with the development of integrate circuit technology. The problem of recognition rate mentioned here is not about the iris itself, but the acquisition of the effective image of the iris. The quality of the iris image has become the key point of the current iris system. The preprocessing of iris recognition involves hardware and software design of the system and in this paper both of the designs are discussed.

关键词： iris recognition image preprocessing Hough transform histogram equalization

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image preprocessing for improving OCR accuracy

Image preprocessing for improving OCR accuracy

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3rd International Conference of Young Scientists (MEMSTECH 2007)

作者： Bieniecki, Wojciech Grabowski, Szymon Rozenberg, Wojciech Tech Univ Lodz Comp Engn Dept Al Politech 11 Lodz Poland

ISBN: (纸本)9789665536147

Digital cameras are convenient image acquisition devices: they are fast, versatile, mobile, do not touch the object, and are relatively cheap. In OCR applications, however, digital cameras suffer from a number of limitations, like geometrical distortions. In this paper, we deal with the preprocessing step before text recognition, specifically with images from a digital camera. Experiments, performed with the FineReader 7.0 software as the back-end recognition tool, confirm importance of image preprocessing in OCR applications.

关键词： image preprocessing OCR digital cameras

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image preprocessing to improve Acid-Fast Bacilli (AFB) detection in smear microscopy to diagnose pulmonary tuberculosis

Image preprocessing to improve Acid-Fast Bacilli (AFB) detec...

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29th International Conference on Electronics, Communications and Computers (CONIELECOMP)

作者： Luis Diaz-Huerta, Jorge del Carmen Tellez-Anguiano, Adriana Antonio Gutierrez-Gnecchi, Jose Yair Colin-Gonzalez, Owen Lucia Zavala-Santoyo, Fanny Arellano-Calderon, Sergio TecNM IT Morelia Div Estudios Posgrad & Invest Morelia Mich Mexico LESPM Lab Micobaterias Morelia Mich Mexico

ISBN: (纸本)9781728111452

Pulmonary tuberculosis (TB) is a highly infectious disease. TB is curable if it is diagnosed opportunely. Worldwide, the most used diagnostic method is the analysis of smear microscopy, which consists in, using a microscope, detecting and counting the bacilli in the smear. The automatic detection of pulmonary tuberculosis usually involves processing and analyzing digital images related to smear microscopy. The main problem in this analysis is the color variation and low contrast in the images. This paper presents a quick and easy method to minimize these variations by using image preprocessing, changing the RGB color space to the HSV space, analyzing and modifying the original images characteristics to standardize them. The results are validated by using a further segmentation step of the images using Artificial Neural Networks (ANNs) and comparing the results obtained with and without the image preprocessing method.

关键词： Mycobacterium tuberculosis Diagnostic image preprocessing Smear microscopy

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image preprocessing and trajectory feature extraction based on Hidden Markov Models for sign language recognition

Image preprocessing and trajectory feature extraction based ...

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9th International Conference on Software Engineering Artificial Intelligence, Networking and Parallel/Distributed Computing

作者： Van Hieu, Duong Nitsuwat, Supot King Mongkuts Univ Technol Fac Informat Technol Bangkok Thailand

ISBN: (纸本)9780769532639

This paper presents a new image preprocessing and revised feature extraction methods for sign language recognition (SLR) based on Hidden Markov Models (HMMs). Multi-layer Neural Network is used for building an approximate skin model by using Cb and Cr color components of sample pixels. Gesture videos are spitted into image sequences and converted into YCbCr color space. In order to get only hand area in each image, unexpected skin areas such as face of actor and noises are identified and eliminated. After obtaining hand areas from image sequence of each gesture, features such as direction, center of gravity, length, and so on will be taken out for learning and testing phases. The features will be normalized before used as inputs of HMMs for learning models and recognizing gesture activities.

关键词： image preprocessing feature extraction Hidden Markov Model sign language recognition

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