In this paper, a deep learning-based machinevision approach is proposed to automatically detect and classify defective tiles in the production assembly line of a tile manufacturing industry. The deep learning model u...
详细信息
In this paper, a deep learning-based machinevision approach is proposed to automatically detect and classify defective tiles in the production assembly line of a tile manufacturing industry. The deep learning model used in this methodology is trained with 30,000 real-time images of cement/ceramic tiles, and the features of the image samples are extracted using the convolutional layers in the model. The defective tiles are identified and classified using an optimized activation function that acts as the decision-making layer or output layer of the deep learning model. The performance of this deep learning technique is evaluated using various metrics like accuracy, precision, recall and f1-score which is further compared with state-of-the-art activation functions like Relu, sigmoid, tanh and softmax. To further enhance the performance metrics, the feature extraction is done using various pre-trained models like vGG-16, Resnet50 and Inceptionv3 and was further evaluated using metrics like K (Kappa statistic), OA (overall accuracy) and AA (average accuracy). The obtained experimental results with an accuracy of 99.96% under a favorable learning rate prove the robustness and efficiency of the proposed methodology to enhance industrial quality control in any tile manufacturing industry.
In a controlled indoor environment,line tracking has become the most practical and reliable navigation strategy for autonomous mobile robots.A line tracking robot is a self-mobile machine that can recognize and track ...
详细信息
In a controlled indoor environment,line tracking has become the most practical and reliable navigation strategy for autonomous mobile robots.A line tracking robot is a self-mobile machine that can recognize and track a painted line on *** general,the path is set and can be visible,such as a black line on a white surface with high contrasting *** robot’s path is marked by a distinct line or track,which the robot follows to *** scientific contributions from the disciplines of vision and control have been made to mobile robot vision-based ***,automated map generation,autonomous navigation and path tracking is all becoming more frequent in visionapplications.A visual navigation line tracking robot should detect the line with a camera using an imageprocessing *** paper focuses on combining computer vision techniques with a proportional-integral-derivative(PID)control-ler for automatic steering and speed control.A prototype line tracking robot is used to evaluate the proposed control strategy.
Integrating information from vision and language modalities has sparked interesting applications in the fields of computer vision and natural language processing. Existing methods, though promising in tasks like image...
详细信息
Integrating information from vision and language modalities has sparked interesting applications in the fields of computer vision and natural language processing. Existing methods, though promising in tasks like image captioning and visual question answering, face challenges in understanding real-life issues and offering step-by-step solutions. In particular, they typically limit their scope to solutions with a sequential structure, thus ignoring complex inter-step dependencies. To bridge this gap, we propose a graph-based approach to vision-language problem solving. It leverages a novel integrated attention mechanism that jointly considers the importance of features within each step as well as across multiple steps. Together with a graph neural network method, this attention mechanism can be progressively learned to predict sequential and non-sequential solution graphs depending on the characterization of the problem-solving process. To tightly couple attention with the problem-solving procedure, we further design new learning objectives with attention metrics that quantify this integrated attention, which better aligns visual and language information within steps, and more accurately captures information flow between steps. Experimental results on visualHow, a comprehensive dataset of varying solution structures, show significant improvements in predicting steps and dependencies, demonstrating the effectiveness of our approach in tackling various vision-language problems.
Computer vision plays a crucial role in current technological development, understanding a scene from the properties of 2D images. This research line becomes valuable in sports applications, where the scenario can be ...
详细信息
Computer vision plays a crucial role in current technological development, understanding a scene from the properties of 2D images. This research line becomes valuable in sports applications, where the scenario can be challenging to take technical decisions only from the observation. This work aims to develop a system based on computer vision for analyzing tennis games. The implemented method captures videos during the game through cameras installed on the court. machine learning methods and morphological operations will be used over the images to locate the ball position, the court lines and the players location. In addition, the algorithm determines the moment the ball bounces during the game and analyzes whether it occurred in or out of the field. These data are available to players and judges through an Android application, allowing all processed data to be accessed from mobile devices, providing the results quickly and accessible to the user. From the results obtained, the system demonstrated robustness and reliability.
Conventional cameras capture image irradiance (RAW) on a sensor and convert it to RGB images using an image signal processor (ISP). The images can then be used for photography or visual computing tasks in a variety of...
详细信息
Conventional cameras capture image irradiance (RAW) on a sensor and convert it to RGB images using an image signal processor (ISP). The images can then be used for photography or visual computing tasks in a variety of applications, such as public safety surveillance and autonomous driving. One can argue that since RAW images contain all the captured information, the conversion of RAW to RGB using an ISP is not necessary for visual computing. In this paper, we propose a novel rho-vision framework to perform high-level semantic understanding and low-level compression using RAW images without the ISP subsystem used for decades. Considering the scarcity of available RAW image datasets, we first develop an unpaired CycleR2R network based on unsupervised CycleGAN to train modular unrolled ISP and inverse ISP (invISP) models using unpaired RAW and RGB images. We can then flexibly generate simulated RAW images (simRAW) using any existing RGB image dataset and finetune different models originally trained in the RGB domain to process real-world camera RAW images. We demonstrate object detection and image compression capabilities in RAW-domain using RAW-domain YOLOv3 and RAW image compressor (RIC) on camera snapshots. Quantitative results reveal that RAW-domain task inference provides better detection accuracy and compression efficiency compared to that in the RGB domain. Furthermore, the proposed rho-vision generalizes across various camera sensors and different task-specific models. An added benefit of employing the rho-vision is the elimination of the need for ISP, leading to potential reductions in computations and processing times.
The pet market is getting growth rapidly in the world, and the ornamental fish occupy the third in the market ranking, behind dogs and cats. According to the statistics of the Ornamental Fish Association, Taiwan has e...
详细信息
The pet market is getting growth rapidly in the world, and the ornamental fish occupy the third in the market ranking, behind dogs and cats. According to the statistics of the Ornamental Fish Association, Taiwan has exported 18 million ornamental shrimps annually since 2010. Almost six-tenths of global ornamental shrimps are from Taiwan. OpenCv (Open Source Computer vision) provides plenty of machinevisionapplications and often cooperates with the Raspberry Pi to enhance the use of machine engineering for commercial products. This research is, therefore, mainly designed to apply the machinevision to undertake the counting of shrimps automatically. The steps of imageprocessing for accurately counting shrimps are as follows: (1) read the image graphic, (2) filter and remain the sampling color, (3) threshold the image, (4) contour the shrimps in the image (5) count the number. Concerning the performance and reliability, we process the image using Amazon Web Service (AWS) Lambda function. Experimental results of counting shrimps (Neocaridina heteropoda var. red) show that it takes 0.1 s to count 150 shrimps and the precision rate is about 95%.
machinevision is a technology and method used to provide automated image-driven analysis in applications such as inspection, process control, and guidance, and is very popular in industries nowadays. Computer/machine...
详细信息
With emergence of Internet of Things (IoT) and subsequent technologies, smart devices are being increasingly used in various domains such as smart homes, smart parking, intelligent transportation etc. vast amount of i...
详细信息
With emergence of Internet of Things (IoT) and subsequent technologies, smart devices are being increasingly used in various domains such as smart homes, smart parking, intelligent transportation etc. vast amount of image and video data has been produced by IoT based systems in the form of continuous and possibly infinite image and video streams. This demands the development of advanced predictive vision systems which exploits stream mining concepts for intelligent processing of visual data streams. Among other challenges faced by visual data streams, a major challenge is concept drift, which is caused by overtime change in data distribution. In the presence of skewed data, the detection of concept drift becomes more challenging. When analyzing the data generated from smart devices and other performance critical wireless sensors, concept drift affects data integrity and accuracy of prediction results. EWMA for Concept Drift Detection (ECDD) has been proposed in the literature for detecting data streams. However, ECDD has a high prediction error rate which makes it less useful for performance critical data streams generated by imaging and video data streams. In this paper, vision based Drift Detection Method (visDDM) is proposed, which systematically handles abrupt and gradual concept drift in data streams. Experiments have been performed using synthetic and real world datasets from different application domains. Our proposed visDDM algorithm is able to handle abrupt and gradual drift types and outperformed the existing drift detection methods in terms of accuracy and mean evaluation time.
vision Studio aims to utilize a diverse range of modern deep learning and computer vision principles and techniques to provide a broad array of functionalities in image and video processing. Deep learning is a distinc...
详细信息
In order to solve the problem of crop disease detection in large-scale planting, a new crop disease detection algorithm based on multi-feature decision fusion is proposed. This paper proposes a multi-feature decision ...
详细信息
In order to solve the problem of crop disease detection in large-scale planting, a new crop disease detection algorithm based on multi-feature decision fusion is proposed. This paper proposes a multi-feature decision fusion disease discrimination algorithm (PD R-CNN) based on machinevision on crop surfaces. The algorithm is based on the machinevisionprocessing model of R-CNN and integrates a disease discrimination algorithm on the basis of R-CNN. After training on crop image data sets, PD R-CNN can reach the goal of identifying crop surface lesions. This paper uses machinevisionimage acquisition, imageprocessing and analysis technology to collect and analyze the growth of cucumber seedlings. The research results show that compared with manual judgment, PD R-CNN reduces the workload and can effectively distinguish crop diseases. Through experiments, during the occurrence of pests and diseases, PD R-CNN has a monitoring accuracy of 88.0% for mosaic disease, 92.0% for root rot, 88.0% for powdery mildew, and 86.0% for aphids, indicating that there are errors in actual monitoring, but the accuracy exceeds 85.0% can be put into use.
暂无评论