Automatic License Plate Recognition systems aim to provide a solution for detecting, localizing, and recognizing license plate characters from vehicles appearing in video frames. However, deploying such systems in the...
详细信息
ISBN:
(纸本)9781665458245
Automatic License Plate Recognition systems aim to provide a solution for detecting, localizing, and recognizing license plate characters from vehicles appearing in video frames. However, deploying such systems in the real world requires real-time performance in low-resource environments. In our paper, we propose a two-stage detection pipeline paired with vision API that provides real-time inference speed along with consistently accurate detection and recognition performance. We used a haar-cascade classifier as a filter on top of our backbone MobileNet SSDv2 detection model. This reduces inference time by only focusing on high confidence detections and using them for recognition. We also impose a temporal frame separation strategy to distinguish between multiple vehicle license plates in the same clip. Furthermore, there are no publicly available Bangla license plate datasets, for which we created an image dataset and a video dataset containing license plates in the wild. We trained our models on the image dataset and achieved an AP(0.5) score of 86% and tested our pipeline on the video dataset and observed reasonable detection and recognition performance (82.7% detection rate, and 60.8% OCR F1 score) with real-time processing speed (27.2 frames per second).
The increasing deployment of Advanced Driver Assistance Systems (ADAS) alongside the continual rise in camera sensor resolution has led to high bandwidth, and generally high cost, computation, and intra-vehicle commun...
详细信息
Autonomous terrain classification is an important problem in planetary navigation, whether the goal is to identify scientific sites of interest or to traverse treacherous areas safely. Past Martian rovers have relied ...
详细信息
This paper introduces a high dynamic range pixel for early visionprocessing. Early vision is the first stage to subsequently extract semantic information for imageprocessing or video analytics. This paper proposes t...
详细信息
ISBN:
(数字)9798350365504
ISBN:
(纸本)9798350365511
This paper introduces a high dynamic range pixel for early visionprocessing. Early vision is the first stage to subsequently extract semantic information for imageprocessing or video analytics. This paper proposes to bring said processing to the focal plane, next to a high dynamic range image sensor working on the principle of lateral overflow capacitor. This brings the benefits of processing scenes with a wide dynamic range in a power efficient manner. Circuit simulations for edge detection, as an example of early visionprocessing conveyed in this paper, show that our proposal meets the accuracy typically found in applications like machinevision. Simulations are in XFAB’s XS018 technology.
In the world, several sign languages (SL) are used, and BSL (Baby Sign Language) is the process of communication between the parents and baby using gestures. Communication by gestures is a non-verbal process that util...
详细信息
In the world, several sign languages (SL) are used, and BSL (Baby Sign Language) is the process of communication between the parents and baby using gestures. Communication by gestures is a non-verbal process that utilizes motion to pass on realities, expressions and feelings to people. SL is the communication mode in which the information is conveyed via movement of body parts like cheeks, eyebrows and head. Even though many research works based on SL are available, research in BSL remains a challenge. Hence, this paper presents an optimization-based automated recognition of the deep BSL system, which determines the gesture signalled by the kids. Initially, the image frames are extracted from the videos and data augmentation processes are performed. After pre-processing, the features are extracted from the frames using the Enhanced Convolution Neural Network (ECNN). The optimal characteristics are then selected by a new Life Choice Based Optimizer (LCBO). Finally, the classification is carried out by the Deep Long Short-Term Memory (DLSTM) scheme. The implementation is performed on the Python platform, and the performances are evaluated using several performance metrics such as accuracy, precision, kappa, f1-score and recall. The performance of the proposed approach (ECNN-DLSTM) is compared with several deep and machine learning approaches and obtains an accuracy of 99% and a kappa of 96%.
Accuracy and computational time are two crucial parameters influencing the efficacy of classification algorithms for remote sensing applications. machine learning algorithms are known for achieving notable success for...
详细信息
Accuracy and computational time are two crucial parameters influencing the efficacy of classification algorithms for remote sensing applications. machine learning algorithms are known for achieving notable success for several classification problems in various domains, including remote sensing. However, they are well-recognized and considered accurate and efficient for closed-set recognition (CSR) but may provide suboptimal and erroneous results for open-set recognition (OSR) tasks. Many practical image-driven and computer visionapplications have open-set and dynamic scenarios with unknown data where classification algorithms have not yet achieved significant prediction performance. This paper presents a group of class-aware (CA) classification algorithms based on a supervised cascaded classifier system ((SCS)-S-2), called CA-(SCS)-S-2, which is accurate for OSR and CSR tasks. We evaluate the prediction accuracy of the proposed methods against the state-of-the-art methods in a multiclass setting using multiple image classification scenarios of OSR and CSR. The test case scenarios use six multispectral and hyperspectral datasets from different sensing platforms. And to assess the computational performance of the methods, we designed various field-programmable gate array (FPGA) architectures of the proposed methods. We evaluated their real-time performance on a low-cost, low-power Artix-7 35 T FPGA.
作者：
Ngo, Ha Quang ThinhBui, The Tri
268 Ly Thuong Kiet Street District 10 Ho Chi Minh City Viet Nam
Linh Trung Ward Thu Duc City Ho Chi Minh City Viet Nam
Applying imageprocessing to electromechanical systems is a problem of interest to scientists, in order to serve humans in many fields. To do that, there needs to be a connection between imageprocessing and mechanica...
详细信息
Pigment epithelial detachment(PED) is a disorder in retina that happens when RPE layers of cells at the back side of the eye come apart, or get teared. The bend of layers in the retina, as well as fluid, proteins, tis...
详细信息
ISBN:
(纸本)9781665495158
Pigment epithelial detachment(PED) is a disorder in retina that happens when RPE layers of cells at the back side of the eye come apart, or get teared. The bend of layers in the retina, as well as fluid, proteins, tissue, or blood vessels, is a defining feature of PED disease, which occurs most frequently in the macula. PED can disturb the vision of the people which is often depict dark shadow, blurry vision or partial loss of vision. The optical coherence tomography (OCT) is a trend set of high resolution and non-invasive imaging modality that expedite the structure of the retina. OCT non-invasively yields cross-sectional volume of images with tissues. The major objective of this research paper is to study, state of art and to classify the retinal layer segmentation techniques, PED fluid segmentation and classification of diseases in retinal OCT images. The medical industry is suffering with more critical patients and the cases are increasing in eye diseases double the number as of now. The artificial intelligence (AI) techniques help the health sector with a great and accurate automatic detection of disease. The image classification and pattern recognition are transforming the industry with artificial intelligence techniques. Many studies are being conducted employing imageprocessing to aid in the early diagnosis of this disease. imageprocessing techniques have advanced as a result of the introduction of artificial intelligence and machine learning. In this review paper, the structure classification methods and the image segmentation method that are best available existing research is discussed. This review summarizes all the recent algorithms that suits for the application of machine learning algorithms for predicting retinal diseases in OCT images. The algorithms discussed from existing research paper, produce the readers to identify the best accurate algorithm for retinal classification of infected eye and normal eye, precision and less processing time for la
image segmentation plays an important role in computer vision technology and agriculture is one of their applications. The crop images present near the vicinity are complex and dense. Hence, multilevel thresholding of...
详细信息
暂无评论