Multi-Feature Fusion and Enhancement Single Shot Detector for Traffic Sign Recognition

Jin, Yanmei; Fu, Yusheng; Wang, Wen-Qin; Ren, Chunhui; Xiang, Xin

doi:10.1109/access.2020.2975828

Cited by 69 publications

(47 citation statements)

References 38 publications

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“…Li [16] relied on edge information to recognize traffic signs that are difficult to detect in the driving environment: Based on the shape features of scale-invariant edge turning angles, the nonparametric shape detector was used to detect circles, triangles, and rectangles in the image; more than 95% of all traffic signs were covered by this detector. Jin et al [17] derived a two-module detector from the multi-feature fusion traffic sign recognition method: the first module extracts the ROIs, using the commonality of symbol boundaries; the latter verifies the effectiveness of the extracted ROIs, and combines HOG and SVM to detect traffic signs. Zheng et al [18] presented a sliding window detection method, which searches for traffic signs on different scales with the integrated channel feature classifier.…”

Section: A Shape-based Traffic Sign Recognitionmentioning

confidence: 99%

Automatic Recognition of Traffic Signs Based on Visual Inspection

Chen

Zhang

et al. 2021

IEEE Access

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The automatic recognition of traffic signs is essential to autonomous driving, assisted driving, and driving safety. Currently, convolutional neural network (CNN) is the most popular deep learning algorithm in traffic sign recognition. However, the CNN cannot capture the poses, perspectives, and directions of the image, nor accurately recognize traffic signs from different perspectives. To solve the problem, the authors presented an automatic recognition algorithm for traffic signs based on visual inspection. For the accuracy of visual inspection, a region of interest (ROI) extraction method was designed through content analysis and key information recognition. Besides, a Histogram of Oriented Gradients (HOG) method was developed for image detection to prevent projection distortion. Furthermore, a traffic sign recognition learning architecture was created based on CapsNet, which relies on neurons to represent target parameters like dynamic routing, path pose and direction, and effectively capture the traffic sign information from different angles or directions. Finally, our model was compared with several baseline methods through experiments on LISA (Laboratory for Intelligent and Safe Automobiles) traffic sign dataset. The model performance was measured by mean average precision (MAP), time, memory, floating point operations per second (FLOPS), and parameter number. The results show that our model consumed shorter time yet better recognition performance than baseline methods, including CNN, support vector machine (SVM), and regionbased fully convolutional network (R-FCN) ResNet 101.

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Section: A Shape-based Traffic Sign Recognitionmentioning

confidence: 99%

Automatic Recognition of Traffic Signs Based on Visual Inspection

Chen

Zhang

et al. 2021

IEEE Access

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“…Based on this situation, the one-stage recognition algorithm is widely studied. Jin et al [23] propose a traffic sign recognition method based on modified SSD which uses convolutional pyramid feature maps for traffic sign recognition. This algorithm can reuse the multiscale feature maps from different layers calculated in the forward pass.…”

Section: The State-of-the-art Traffic Sign Recognition Algorithmmentioning

confidence: 99%

“…FPS (Frames Per Second) is the number of images that can be recognized by the recognition network per second and is the metric for judging the recognition speed of the network. The larger its value, the faster the speed of recognition [ 13 , 23 ]. Figure 1 presents the holistic process of the traffic sign recognition algorithm.…”

Section: Proposed Traffic Sign Recognition Algorithmmentioning

confidence: 99%

“… Visual comparison of recognition performance of different traffic sign recognition algorithms for the same image. ( a – f ) are the recognition results of the traffic sign recognition algorithms based on modified Faster R-CNN [ 9 ], modified SSD [ 23 ], modified RefineDet [ 26 ], modified RetinaNet101 [ 39 ] (its backbone is ResNet-101), modified YOLOv3 [ 10 ] and the proposed method. …”

Section: Figurementioning

confidence: 99%

“…Objective evaluation of the performance of different traffic sign recognition algorithms. 1% 59.8% 42.7% 18.4% 43.2% 50.0% Method B[23] 100.4M 9 60.3% 29.4% 49.4% 30.6% 10.5% 34.1% 43.6% Among them, FPS (Frames Per Second) is the metric for judging the recognition speed of the network. The larger its value, the faster the recognition speed of the network.…”

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confidence: 99%

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A Multiscale Recognition Method for the Optimization of Traffic Signs Using GMM and Category Quality Focal Loss

Gao

Chen

Shi

et al. 2020

Sensors

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Effective traffic sign recognition algorithms can assist drivers or automatic driving systems in detecting and recognizing traffic signs in real-time. This paper proposes a multiscale recognition method for traffic signs based on the Gaussian Mixture Model (GMM) and Category Quality Focal Loss (CQFL) to enhance recognition speed and recognition accuracy. Specifically, GMM is utilized to cluster the prior anchors, which are in favor of reducing the clustering error. Meanwhile, considering the most common issue in supervised learning (i.e., the imbalance of data set categories), the category proportion factor is introduced into Quality Focal Loss, which is referred to as CQFL. Furthermore, a five-scale recognition network with a prior anchor allocation strategy is designed for small target objects i.e., traffic sign recognition. Combining five existing tricks, the best speed and accuracy tradeoff on our data set (40.1% mAP and 15 FPS on a single 1080Ti GPU), can be achieved. The experimental results demonstrate that the proposed method is superior to the existing mainstream algorithms, in terms of recognition accuracy and recognition speed.

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An FPGA‐based tool for supporting the design, modeling, and evaluation of hybrid object recognition systems on computer engineering courses

Guzmán‐Ramírez,

Garcia,

Pacheco

et al. 2024

Comp Applic In Engineering

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The field of computer vision is characterized by computationally intensive algorithms and techniques with strict real‐time requirements. Field programmable gate arrays (FPGAs) are based on a concurrent paradigm which allows the design of efficient hardware architectures and has positioned FPGAs as an ideal device for implementing compute‐intensive applications. For this reason, FPGA technology has had a great impact in areas such as computer vision, where one of the main objectives for researchers working in this field is to create efficient automatic object recognition systems. Therefore, the need to provide undergraduates with the necessary skills to design FPGA‐based object recognition systems is evident. With this aim in mind, it is essential that specialization courses related to the design of these systems include the required resources for the student to apply the theoretical knowledge in solving practical problems. In this article, we present a development tool designed to help students, teachers, and researchers during the design‐modeling‐implementation process of object recognition systems based on FPGAs. The proposed tool operates under a modular approach as this facilitates the working on any of the phases of a recognition system and it is considered as a hybrid because the other phases can be developed using a software language. An empirical evaluation involving undergraduates enrolled in a Computer Engineering program was conducted to create a hardware architecture for the DAISY descriptor that uses the homogeneous features of objects immersed in images to produce an efficient representation. By considering similar descriptors such as Scale‐Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG), DAISY is computed by convolving orientation maps instead of using weighted sums of gradient norms, which offers the same kind of invariance at a lower computational cost for the dense case. The results obtained during such an evaluation indicated that students consider this FPGA‐based tool to be an alternative to receiving practical training on designing systems for solving problems related to the area of object recognition.

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Multi-Feature Fusion and Enhancement Single Shot Detector for Traffic Sign Recognition

Cited by 69 publications

References 38 publications

Automatic Recognition of Traffic Signs Based on Visual Inspection

Automatic Recognition of Traffic Signs Based on Visual Inspection

A Multiscale Recognition Method for the Optimization of Traffic Signs Using GMM and Category Quality Focal Loss

An FPGA‐based tool for supporting the design, modeling, and evaluation of hybrid object recognition systems on computer engineering courses

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