Traffic sign recognition is crucial for the safe and efficient operation of autonomous vehicles. While previous research has primarily focused on traffic sign recognition in foreign countries, these studies often face...
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(纸本)9798350352368
Traffic sign recognition is crucial for the safe and efficient operation of autonomous vehicles. While previous research has primarily focused on traffic sign recognition in foreign countries, these studies often face limitations such as differing traffic sign designs, language barriers in textual information, and varying environmental conditions. In this paper, we propose a traffic sign detection and recognition system tailored for Malaysia, utilizing Convolutional Neural Networks (CNNs) and Optical Character Recognition (OCR). In this paper, we propose a traffic sign detection and recognition system utilizing You Only Look Once (YOLO) v8 for object detection and EasyOCR to process textual information on selected traffic signs. Our system achieves a mean Average Precision (mAP) of 0.824 and an average processing time of 1.2 seconds per frame, which is comparable to existing literature. Furthermore, the complexity of our method is significantly reduced, enhancing its potential for real-time processingapplications, as evidenced by its efficient processing time.
The realization of automatic operation of production by the industrial Internet of Things needs the functional assistance of machinevision technology. Different from the recognition and detection of some known featur...
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The realization of automatic operation of production by the industrial Internet of Things needs the functional assistance of machinevision technology. Different from the recognition and detection of some known features, it is difficult to realize defect detection in machinevisionapplications. Therefore, this article studies the industrial production defect detection method based on machinevision technology in industrial Internet of Things. Firstly, in the second chapter, the images of industrial products collected by machinevision system are preprocessed and thinned to obtain more ideal detection accuracy and measurement accuracy. The methods of image binarization, morphological processing, thinning and burr elimination are given in detail. In the third chapter, product defect detection model is constructed based on U-Net network, and residual structure, hole convolution module, strip pooling module and attention mechanism module are introduced to optimize the network model. Experimental results verify the effectiveness of the model for product defect detection.
Quantitatively defining the relationship between laser powder bed fusion(LPBF)process parameters and the resultant microstructures for LPBF fabricated alloys is one of main research *** date,achieving the desired micr...
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Quantitatively defining the relationship between laser powder bed fusion(LPBF)process parameters and the resultant microstructures for LPBF fabricated alloys is one of main research *** date,achieving the desired microstructures and mechanical properties for LPBF alloys is generally done by time-consuming and costly trial-and-error experiments that are guided by human ***,we develop an approach whereby an image-driven conditional generative adversarial network(cGAN)machine learning model is used to reconstruct and quantitatively predict the key microstructural features(e.g.,the morphology of martensite and the size of primary and secondary martensite)for LPBF fabricated *** results demonstrate that the developed image-driven machine learning model can effectively and efficiently reconstruct micrographs of the microstructures within the training dataset and predict the microstructural features beyond the training dataset fabricated by different LPBF parameters(i.e.,laser power and laser scan speed).This study opens an opportunity to establish and quantify the relationship between processing parameters and microstructure in LPBF Ti-6Al-4v using a GAN machine learning-based model,which can be readily extended to other metal alloy systems,thus offering great potential in applications related to process optimisation,material design,and microstructure control in the additive manufacturing field.
Multimedia data is crucial in the military, medical, forensics, social, etc., to transmit a large amount of data. Security of this sensitive information is the primary issue. This paper uses Latin square and machine l...
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Multimedia data is crucial in the military, medical, forensics, social, etc., to transmit a large amount of data. Security of this sensitive information is the primary issue. This paper uses Latin square and machine learning techniques such as neural networks and genetic algorithm to design an image encryption algorithm. A new neural network-based pseudorandom number generator is proposed to generate a chaotic sequence for various applications. Encryption key images are designed using Latin squares in the finite field. Further, the Latin squares are XOR with the input matrix to get the encrypted images. The proposed algorithm is iterated a finite number of times to generate a cipher image population. Randomly two parents are chosen from the generated population, and row and column arrangements produce offspring. A genetic algorithm is the optimization technique used for the best encrypted image search. The pixel correlation value serves as a fitness function. Finally, the least correlated cipher image is obtained from the genetic algorithm applied to the parent and offspring of the population generated from the encryption algorithm. The simulation results from the proposed image encryption model surpass many communication channel attacks and perform better when compared to existing image security algorithms.
Particulate matter in the atmosphere obscures the visibility of the atmosphere, causing a condition known as haze. Other natural phenomena like mist, fog, and dust also obscure the vision;this is because of scattering...
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The use of AI technologies in remote sensing (RS) tasks has been the focus of many individuals in both the professional and academic domains. Having more accessible interfaces and tools that allow people of little or ...
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The use of AI technologies in remote sensing (RS) tasks has been the focus of many individuals in both the professional and academic domains. Having more accessible interfaces and tools that allow people of little or no experience to intuitively interact with RS data of multiple formats is a potential provided by this integration. However, the use of AI and AI agents to help automate RS-related tasks is still in its infancy stage, with some frameworks and interfaces built on top of well-known vision language models (vLM) such as GPT-4, segment anything model (SAM), and grounding DINO. These tools do promise and draw guidelines on the potentials and limitations of existing solutions concerning the use of said models. In this work, the state of the art AI foundation models (FM) are reviewed and used in a multi-modal manner to ingest RS imagery input and perform zero-shot object detection using natural language. The natural language input is then used to define the classes or labels the model should look for, then, both inputs are fed to the pipeline. The pipeline presented in this work makes up for the shortcomings of the general knowledge FMs by stacking pre-processing and post-processingapplications on top of the FMs;these applications include tiling to produce uniform patches of the original image for faster detection, outlier rejection of redundant bounding boxes using statistical and machine learning methods. The pipeline was tested with UAv, aerial and satellite images taken over multiple areas. The accuracy for the semantic segmentation showed improvement from the original 64% to approximately 80%-99% by utilizing the pipeline and techniques proposed in this work. GitHub Repository: MohanadDiab/LangRS.
Crops and weeds are involved in a continuous competition for equal resources, which may result in a potential decrease in crop yields by up to 31% and an increase in the costs of agricultural inputs by up to 22% of cu...
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Crops and weeds are involved in a continuous competition for equal resources, which may result in a potential decrease in crop yields by up to 31% and an increase in the costs of agricultural inputs by up to 22% of cultivation. Weeds further impact crop production, and their detection is crucial for effective crop management. In this research, we targeted common weeds of cotton field, specifically i) Digitaria sanguinalis (L.) Scop, ii) Amaranthus retroflexus L., iii) Acalypha australis, L., iv) Cephalanoplos segetum, and v) Chenopodium album L. Additionally, imageprocessing techniques such as grayscale conversion, binarization, and Gaussian and morphological filters were also utilized. These methods are based on machinevision and facilitate rapid and straightforward weed detection by segmenting, scrutinizing, and comparing input images. The plant height and area were obtained during cotton planting within 32 days and fitted to develop the growth law concerning planting days for achieving the function of distinguishing cotton from weeds. We conducted recognition experiments by dividing images into four quadrants and categorizing weeds as either inter-row or intra-row. Meanwhile, the inter-row planting information was used to identify weeds, and the leaf pixel area and circularity were used as the identification methods for intra-row weeds, which reduced the algorithm's running time and improved real-time performance. The experimental results indicated that the inter-row weed recognition rate was 89.4%, with an average processing time of 102ms. Whereas in the case of intra-row weeds, the recognition rate was measured at 84.6%, and the overall recognition rate for cotton was 85.0%, with a mean time consumption of 437ms. Furthermore, the present research underscores recent advancements such as machinevision and high-resolution imaging, which have significantly improved the accuracy of automated weed identification in cotton fields while acknowledging ongoing challen
When considering sparse motion capture marker data, one typically struggles to balance its overfitting via a high dimensional blendshape system versus underfitting caused by smoothness constraints. With the current tr...
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When considering sparse motion capture marker data, one typically struggles to balance its overfitting via a high dimensional blendshape system versus underfitting caused by smoothness constraints. With the current trend towards using more and more data, our aim is not to fit the motion capture markers with a parameterized (blendshape) model or to smoothly interpolate a surface through the marker positions, but rather to find an instance in the high resolution dataset that contains local geometry to fit each marker. Just as is true for typical machine learning applications, this approach benefits from a plethora of data, and thus we also consider augmenting the dataset via specially designed physical simulations that target the high resolution dataset such that the simulation output lies on the same so-called manifold as the data targeted.
Neural Radiance Fields (NeRF) rendering is a promising Artificial intelligence (AI) technology for generating photorealistic views, with significant potential for automotive applications. However, traditional metrics ...
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machinevisionapplications for intelligent vision systems in manufacturing industries were reported based on imageprocessing and artificial intelligence technology. We propose the imaging and vision development plat...
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