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•The impact of various image preprocessing on machine learning using micrograph dataset is evaluated for the first time.•Color , size variation, normalization and image enhancement ...are all considered for both classification and regression tasks.•The impact of preprocessing methods on the machine learn is contingent upon the dataset characteristics and task type.
Appropriate image preprocessing could improve machine learning performance, but the robustness of machine learning to preprocessing methods in micrograph datasets with significant sample differences remains unexplored. Here we collected hundreds of optical micrographs varied in color, contrast, size and brightness and tensile strength from published literature. After preprocessing including color transformation, size adjustment, normalization and image enhancement, classification model of whether it was texture or not and tensile strength prediction model were established using transfer learning and VGG16 model. Our results showed comparable accuracy between classification models using grayscale and color micrographs. Based on grayscale images, classification model utilizing center cropping achieved an average accuracy 1.09% higher than model employing scaling, while coefficient of determination (R2) of regression model showed an average increase of 0.0404. The combination of cropping and dividing by 255 was preferable for classification model, yielding an accuracy of 90.79%. Similarly, the combination of cropping and min–max normalization had a better effect on regression model, yielding R2 of 0.2167. Histogram equalization emerged as the optimal technique for improving classification model, yielding 2.63% increase in accuracy compared to models without image enhancement. For regression models, gamma correction exhibited the highest improvement effect, with an R2 of 0.2681.
With the development of machine learning for decades, there are still many problems unsolved, such as image recognition and location detection, image classification, image generation, speech ...recognition, natural language processing and so on. In the field of deep learning research, the research on image classification has always been the most basic, traditional and urgent research direction. At the same time, computer intelligent image recognition technology is also conducive to gradually better respond to the development of international indicators, and promote the development and progress of various fields. Therefore, image processing technology based on machine learning has been widely used in feature image, classification, segmentation and recognition, and is a hot spot in various fields. However, due to the complexity of video images and the distribution of objects in different application backgrounds, the classification accuracy becomes important and difficult. In the paper transportation industry, image recognition technology is applied to license plate recognition to extract license plate from complex background, segment license plate characters and recognize characters, and construct a machine learning non license plate automatic generation algorithm, which may improve the efficiency of non license plate recognition. The diversity and high generation speed of license plate training sample set can achieve the purpose of effectively training strong classifier. By using genetic algorithm to optimize BP neural network to classify license plate information, the anti-interference ability and license plate recognition accuracy are improved to a certain extent.
•VGGFACE and VGGFACE2 pre-trained models are utilized for cattle face recognition.•A face image database of 91 cattle was constructed, consisting of 315 raw RGB face images.•Pre-processing of RGB ...face images improves the model’s reliability.•Augmenting data with transformed RGB images improves the model’s performance.
Cattle face recognition technology is an innovative approach to herd management that allows farmers to effectively monitor the health of animals, their movement and behavior, for example, by tracking individual animals to determine which ones need extra care or treatment. Tobehaviour address the issue of cattle identification, especially non-invasive facial recognition, deep neural networks are currently being researched in the biometrics sector. However, their implementation presents significant challenges due to the small database of animal faces. The collected dataset contains 315 RGB images of 91 Aberdeen Angus cattle. This paper uses deep transfer learning to design a deep neural network. Based on the pre-trained models, VGGFACE and VGGFACE2, we propose a model for recognizing the faces of cattle. RGB images were used after preprocessed to improve the reliability of face recognition. RGB image preprocessing consists of several steps: contrast and contour enhancement methods, cattle face detection, image resizing, and normalisation. Data augmentation methods using transformation RGB images and neural network fine-tuning were used to train the deep-learning model. The recognition rate of the pre-trained VGGFACE2 model is 97.1% with the collected dataset. The proposed model can lead to increased efficiency and sustainability of livestock production and improved animal welfare conditions.
Most recently, deep learning-based visual detection has attracted rapidly increasing attention paid to marine organisms, thereby expecting to significantly benefit ocean ecology. Suffering from ...underwater visual degradation including low contrast, color distortion and blur, etc., both advances and challenges on visual detection of marine organisms (VDMO) co-exist in the literature. In this survey, deep learning-based VDMO techniques are comprehensively revisited from a systematic viewpoint covering advances in underwater image preprocessing, deep learning-based detection approaches, benchmark dataset and intensively quantitative comparisons. Furthermore, in terms of inherent features of marine organisms and complexity of underwater visual environments, underlying challenges are unfolded in depth. Such a self-contained survey is expected to exploit potential breakthroughs and explore probable trends in deep learning-based VDMO techniques.
Uneven dust accumulation can significantly influence the thermal balance between different regions of photovoltaic (PV) panels, leading to a sharp decrease in power generation efficiency and service ...life. In this paper, a new identification method for uneven dust accumulation on the surface of PV panels is developed to analyze the dust state (concentration and distribution) quantitatively. First, a novel deep residual neural network (DRNN) is proposed to obtain the regional dust concentration. The residual elements in the model are connected in the form of skipping layers to reduce the order of the weight matrices and improve the network flexibility and the feature learning accuracy. Second, an image preprocessing method is designed to classify the dust accumulation. It includes transformation and correction, removal of the silver grid, nonlinear interpolation, equivalent segmentation, and clustering. A new error evaluation method, error loop, is proposed to analyze the consistency between the measured and the experimental results. The results show that the DRNN has better prediction accuracy than other methods. The R2 and mean absolute error (MAE) of the DRNN are 78.7% and 3.67, respectively. In addition, three conditions are used to verify the performance of the identification method for determining uneven dust accumulation. The average evaluation coefficients of the error loop are 1.19, 0.77, and 1.10, respectively, meeting the design requirements. The proposed method can provide theoretical support for the intelligent operation and maintenance of PV systems.
•An identification method for the uneven dust of PV panels is proposed.•A DRNN network architecture of free identity mapping is established.•A multiscale error evaluation method (error loop) is presented.•The model is verified by experiments and error loop evaluation.
We describe the approach that won the final phase of the German traffic sign recognition benchmark. Our method is the only one that achieved a better-than-human recognition rate of 99.46%. We use a ...fast, fully parameterizable GPU implementation of a Deep Neural Network (DNN) that does not require careful design of pre-wired feature extractors, which are rather learned in a supervised way. Combining various DNNs trained on differently preprocessed data into a Multi-Column DNN (MCDNN) further boosts recognition performance, making the system insensitive also to variations in contrast and illumination.
The introduction of deep learning caused a significant breakthrough in digital pathology. Thanks to its capability of mining hidden data patterns in digitised histological slides to resolve ...diagnostic tasks and extract prognostic and predictive information. However, the high performance achieved in classification tasks depends on the availability of large datasets, whose collection and preprocessing are still time-consuming processes. Therefore, strategies to make these steps more efficient are worth investigation. This work introduces SlideTiler, an open-source software with a user-friendly graphical interface. SlideTiler can manage several image preprocessing phases through an intuitive workflow that does not require specific coding skills. The software was designed to provide direct access to virtual slides, allowing custom tiling of specific regions of interest drawn by the user, tile labelling, quality assessment, and direct export to dataset directories. To illustrate the functions and the scalability of SlideTiler, a deep learning-based classifier was implemented to classify 4 different tumour histotypes available in the TCGA repository. The results demonstrate the effectiveness of SlideTiler in facilitating data preprocessing and promoting accessibility to digitised pathology images for research purposes. Considering the increasing interest in deep learning applications of digital pathology, SlideTiler has a positive impact on this field. Moreover, SlideTiler has been conceived as a dynamic tool in constant evolution, and more updated and efficient versions will be released in the future.
As a technique in artificial intelligence, a convolution neural network model has been utilized to extract average surface roughness from the geometric characteristics of a membrane image featuring ...micro- and nanostructures. For surface roughness measurement, e.g. atomic force microscopy and optical profiler, the previous methods have been performed to analyze a porous membrane surface on an interest of region with a few micrometers of the restricted area according to the depth resolution. However, an image from the scanning electron microscope, combined with the feature extraction process, provides clarity on surface roughness for multiple areas with various depth resolutions. Through image preprocessing, the geometric pattern is elucidated by amplifying the disparity in pixel intensity values between the bright and dark regions of the image. The geometric pattern of the binary image and magnitude spectrum confirmed the classification of the surface roughness of images in a categorical scatter plot. A group of cropped images from an original image is used to predict the logarithmic average surface roughness values. The model predicted 4.80 % MAPE for the test dataset. The method of extracting geometric patterns through a feature map-based CNN, combined with a statistical approach, suggests an indirect surface measurement. The process is achieved through a bundle of predicted output data, which helps reduce the randomness error of the structural characteristics. A novel feature extraction approach of CNN with statistical analysis is a valuable method for revealing hidden physical characteristics in surface geometries from irregular pixel patterns in an array of images.
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The fusion technology, combining infrared and visible modes, has the potential to enhance the semantic content of backgrounds, thereby improving scene interpretability. However, most existing image ...fusion algorithms primarily concentrate on the fusion process, often neglecting the importance of preprocessing the source images to enhance their visual fidelity. Additionally, these algorithms frequently overlook the distinct characteristics of infrared and visible modes, leading to suboptimal weight allocations that do not correspond with human perception. To tackle these issues, this paper proposes a fusion algorithm that emphasizes visual fidelity and the rectification of salient information. More specifically, we improve fusion algorithms by designing an adaptive enhancement method based on Taylor approximation and visual compensation, which proves particularly effective in complex environments. Our proposed multi-scale decomposition approach extracts salient information from the transmission map, thereby enriching fusion results with finer details to accentuate target features. Drawing inspiration from the distinctive attributes of infrared and visible image modes, we devise a fusion weight calculation method grounded in similarity measurements to effectively convey significant information from the source images. To validate the effectiveness of our proposed method, we conducted validation experiments using publicly available datasets. Our experimental findings exhibit a prominent advantage over fifteen state-of-the-art fusion algorithms in both subjective and objective assessments. Our code is publicly available at: https://github.com/VCMHE/FVC_SIR.
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