Real-time method for traffic sign detection and recognition based on YOLOv3-tiny with multiscale feature extraction

With the continuous integration of deep learning and the technique of molecular biology, target detection models must accurately detect the position of each cell in the image and classify it correctly. We present a model for the multi‐scale feature fusion of the existing human cell image dataset based on Gaussian mixedly clustering. First, a novel feature extraction network for extracting preliminary features at picture multi scales was presented, which was based on a residual neural network with Instance Normalization and a Mish activation function. Second, the presented model adopts the idea of feature fusion and introduced a new type of feature fusion network to integrate feature graphs on different scales. Furthermore, a Gaussian hybrid clustering algorithm was proposed to cluster the hyperparameters. Based on the experimental results, the average accuracy of the proposed model in the human cell image dataset exceeds 0.96, which improves by 11.9% compared with the existing target detection methods in the same field. Experiments show that the proposed model had been adapted to datasets with uneven sample distribution, providing new ideas for research on medical images.

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“…Therefore, the input image data size of the MFF‐MG model was a multiple of 32. We let all the images in the human cell image dataset be resized to

448 \times 448

and then put them into the model [31].…”

Section: Methodsmentioning

confidence: 99%

Automatic detection of three cell types in a microscope image based on deep learning

Zhang

Zhou

et al. 2022

Journal of Biophotonics

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“…Road Safety: Road safety refers to the efforts used to reduce the likelihood of collisions and protect road users. This includes different efforts and legislation aimed at encouraging safe driving practices, improving road infrastructure, monitoring road conditions, identifying road dangers, and improving traffic management and vehicle safety [35][36][37][38][39][40][41][42][43].…”

Section: Rq1 [Applications]: What Are the Main Applications Of Traffi...mentioning

confidence: 99%

“…Jetson Xavier NX is identified as the most often utilized embedded system, as evidenced by several scholarly sources [63,68,77,135]. Following closely behind are Jetson Nano, which is also referenced in several academic articles [63,77,129], Jetson TX2 [41,79,140], and Jetson Xavier AGX, which is mentioned in at least one academic source [77]. These embedded systems are supported by Nvidia.…”

Section: Comparing Metrics Among Different Versions Of Yolomentioning

confidence: 99%

Traffic Sign Detection and Recognition Using YOLO Object Detection Algorithm: A Systematic Review

Flores-Calero,

Astudillo,

Guevara

et al. 2024

Mathematics

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Context: YOLO (You Look Only Once) is an algorithm based on deep neural networks with real-time object detection capabilities. This state-of-the-art technology is widely available, mainly due to its speed and precision. Since its conception, YOLO has been applied to detect and recognize traffic signs, pedestrians, traffic lights, vehicles, and so on. Objective: The goal of this research is to systematically analyze the YOLO object detection algorithm, applied to traffic sign detection and recognition systems, from five relevant aspects of this technology: applications, datasets, metrics, hardware, and challenges. Method: This study performs a systematic literature review (SLR) of studies on traffic sign detection and recognition using YOLO published in the years 2016–2022. Results: The search found 115 primary studies relevant to the goal of this research. After analyzing these investigations, the following relevant results were obtained. The most common applications of YOLO in this field are vehicular security and intelligent and autonomous vehicles. The majority of the sign datasets used to train, test, and validate YOLO-based systems are publicly available, with an emphasis on datasets from Germany and China. It has also been discovered that most works present sophisticated detection, classification, and processing speed metrics for traffic sign detection and recognition systems by using the different versions of YOLO. In addition, the most popular desktop data processing hardwares are Nvidia RTX 2080 and Titan Tesla V100 and, in the case of embedded or mobile GPU platforms, Jetson Xavier NX. Finally, seven relevant challenges that these systems face when operating in real road conditions have been identified. With this in mind, research has been reclassified to address these challenges in each case. Conclusions: This SLR is the most relevant and current work in the field of technology development applied to the detection and recognition of traffic signs using YOLO. In addition, insights are provided about future work that could be conducted to improve the field.

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“…DarkNet53 is the backbone feature extraction network used by the target detection network YOLOv3 for extracting features with 8, 16, and 32-fold downsampling, respectively [25]. The network structure of DarkNet53 is shown in Figure 1, and this network model combines the deep residual network with DarkNet19, the feature extraction network used by YOLOv2 [26].…”

Section: Darknet53 Convolutional Neural Networkmentioning

confidence: 99%

Hybrid Encrypted Watermarking Algorithm for Medical Images Based on DCT and Improved DarkNet53

Liu

Bhatti

et al. 2023

Electronics

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To solve the problem of robustness of encrypted medical image watermarking algorithms, a zero watermarking algorithm based on the discrete cosine transform (DCT) and an improved DarkNet53 convolutional neural network is proposed. The algorithm targets medical images in the encrypted domain. In this algorithm, DCT is performed on the encrypted medical image to extract 32-bit features as feature 1. DarkNet53, a pre-trained network, was chosen for migration learning for the network model. The network uses a fully connected layer and a regression layer instead of the original Softmax layer and classification layer, changing the original classification network into a regression network with an output of 128. With these transformations, 128-bit features can be extracted from encrypted medical images by this network, and then DCT is performed to extract 32-bit features as feature 2. The fusion of features 1 and 2 can effectively improve the robustness of the algorithm. The experimental results show that the algorithm can accurately distinguish different encrypted medical images and can effectively restore the original information from the encrypted watermarked information under traditional and geometric attacks. Compared with other algorithms, the proposed method demonstrates better robustness and invisibility.

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Real-time method for traffic sign detection and recognition based on YOLOv3-tiny with multiscale feature extraction

Cited by 9 publications

References 24 publications

Automatic detection of three cell types in a microscope image based on deep learning

Automatic detection of three cell types in a microscope image based on deep learning

Traffic Sign Detection and Recognition Using YOLO Object Detection Algorithm: A Systematic Review

Hybrid Encrypted Watermarking Algorithm for Medical Images Based on DCT and Improved DarkNet53

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