Visual Recognition of Traffic Signs in Natural Scenes Based on Improved RetinaNet

Liu, Shangwang; Cai, Tongbo; Tang, Xiufang; Zhang, Yangyang; Wang, Changgeng

doi:10.3390/e24010112

Cited by 14 publications

(5 citation statements)

References 37 publications

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“…Overall, whether integrating the proposed method as an embeddable module into YOLOv6 or YOLOv8 or comparing it directly with methods such as Faster R-CNN [22], RetinaNet [23], CenterNet [24], or HRNet [25] (as shown in Table 5 above), the experimental results are consistently favorable. This indicates the versatility and effectiveness of the proposed method within various YOLO frameworks as well as among other state-of-the-art (SOTA) techniques.…”

Section: Analysis Of Resultsmentioning

confidence: 95%

Research on Multi-Modal Pedestrian Detection and Tracking Algorithm Based on Deep Learning

Zhao,

Hao,

Huo

2024

Future Internet

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In the realm of intelligent transportation, pedestrian detection has witnessed significant advancements. However, it continues to grapple with challenging issues, notably the detection of pedestrians in complex lighting scenarios. Conventional visible light mode imaging is profoundly affected by varying lighting conditions. Under optimal daytime lighting, visibility is enhanced, leading to superior pedestrian detection outcomes. Conversely, under low-light conditions, visible light mode imaging falters due to the inadequate provision of pedestrian target information, resulting in a marked decline in detection efficacy. In this context, infrared light mode imaging emerges as a valuable supplement, bolstering pedestrian information provision. This paper delves into pedestrian detection and tracking algorithms within a multi-modal image framework grounded in deep learning methodologies. Leveraging the YOLOv4 algorithm as a foundation, augmented by a channel stack fusion module, a novel multi-modal pedestrian detection algorithm tailored for intelligent transportation is proposed. This algorithm capitalizes on the fusion of visible and infrared light mode image features to enhance pedestrian detection performance amidst complex road environments. Experimental findings demonstrate that compared to the Visible-YOLOv4 algorithm, renowned for its high performance, the proposed Double-YOLOv4-CSE algorithm exhibits a notable improvement, boasting a 5.0% accuracy rate enhancement and a 6.9% reduction in logarithmic average missing rate. This research’s goal is to ensure that the algorithm can run smoothly even on a low configuration 1080 Ti GPU and to improve the algorithm’s coverage at the application layer, making it affordable and practical for both urban and rural areas. This addresses the broader research problem within the scope of smart cities and remote ends with limited computational power.

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Section: Analysis Of Resultsmentioning

confidence: 95%

Research on Multi-Modal Pedestrian Detection and Tracking Algorithm Based on Deep Learning

Zhao,

Hao,

Huo

2024

Future Internet

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“…Average precision ( AP ) balances the precision ( P ) and recall ( R ) values, reflecting the performance of the model in each class, which is the area under the precision-recall curve, as shown in Equation (14) [ 38 ]. The mAP is the average of AP for target classes, which is used to show the model’s advantages and disadvantages across all classes (Equation (15)).…”

Section: Methodsmentioning

confidence: 99%

“…The mAP is the average of AP for target classes, which is used to show the model’s advantages and disadvantages across all classes (Equation (15)). This study used the value of mAP when the IOU threshold = 0.5 [ 38 , 39 ].

…”

Section: Methodsmentioning

confidence: 99%

Comparison of RetinaNet-Based Single-Target Cascading and Multi-Target Detection Models for Administrative Regions in Network Map Pictures

Che

Wang

et al. 2022

Sensors

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There is a critical need for detection of administrative regions through network map pictures in map censorship tasks, which can be implemented by target detection technology. However, on map images there tend to be numerous administrative regions overlaying map annotations and symbols, thus making it difficult to accurately detect each region. Using a RetinaNet-based target detection model integrating ResNet50 and a feature pyramid network (FPN), this study built a multi-target model and a single-target cascading model from three single-target models by taking Taiwan, Tibet, and the Chinese mainland as target examples. Two models were evaluated both in classification and localization accuracy to investigate their administrative region detection performance. The results show that the single-target cascading model was able to detect more administrative regions, with a higher f1_score of 0.86 and mAP of 0.85 compared to the multi-target model (0.56 and 0.52, respectively). Furthermore, location box size distribution from the single-target cascading model looks more similar to that of manually annotated box sizes, which signifies that the proposed cascading model is superior to the multi-target model. This study is promising in providing support for computer map reading and intelligent map censorship.

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“…In the realm of intelligent city tra c detection, the RetinaNet algorithm stands out with its powerful performance for small targets. It utilizes a loss function called Focal Loss, effectively handling a large number of background targets and providing more accurate results for urban tra c monitoring (Liu et al, 2022). Furthermore, the YOLOv5 (You Only Look Once) algorithm signi cantly improves target detection accuracy in complex scenes by introducing a deeper network structure and a sophisticated feature fusion mechanism, offering robust support for real-time and high-precision detection in smart city tra c scenarios(A. .…”

Section: Introductionmentioning

confidence: 99%

Enhancing Real-time Target Detection in Smart Cities: YOLOv8-DSAF Insights

Li,

Huang,

Tao

2024

Preprint

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With the global rise of smart city construction, target detection technology plays a crucial role in optimizing urban functions and improving the quality of life. However, existing target detection technologies still have shortcomings in terms of accuracy, real-time performance, and adaptability. To address this challenge, this study proposes an innovative target detection model. Our model adopts the structure of YOLOv8-DSAF. The model comprises three key modules: Depthwise Separable Convolution (DSConv), Dual-Path Attention Gate module (DPAG), and Feature Enhancement Module (FEM). Firstly, DSConv technology optimizes computational complexity, enabling real-time target detection within limited hardware resources. Secondly, the DPAG module introduces a dual-channel attention mechanism, allowing the model to selectively focus on crucial areas, thereby improving detection accuracy in high-dynamic traffic scenarios. Finally, the FEM module highlights crucial features to prevent their loss, further enhancing detection accuracy. Experimental results on the KITTI V and Cityscapes datasets indicate that our model outperforms the YOLOv8 model. This suggests that in complex urban traffic scenarios, our model exhibits superior performance with higher detection accuracy and adaptability. We believe that this innovative model will significantly propel the development of smart cities and advance target detection technology.

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Visual Recognition of Traffic Signs in Natural Scenes Based on Improved RetinaNet

Cited by 14 publications

References 37 publications

Research on Multi-Modal Pedestrian Detection and Tracking Algorithm Based on Deep Learning

Research on Multi-Modal Pedestrian Detection and Tracking Algorithm Based on Deep Learning

Comparison of RetinaNet-Based Single-Target Cascading and Multi-Target Detection Models for Administrative Regions in Network Map Pictures

Enhancing Real-time Target Detection in Smart Cities: YOLOv8-DSAF Insights

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