A Deployment Scheme of YOLOv5 with Inference Optimizations Based on the Triton Inference Server

Fang, Jiacong; Liu, Qiong; Li, Jingzheng

doi:10.1109/icccbda51879.2021.9442557

Cited by 17 publications

(9 citation statements)

References 11 publications

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“…In this paper, the evaluation metrics used include precision, recall and mean average precision. 9 The source code used for the analysis can be found in the Software availability . 10 …”

Section: Methodsmentioning

confidence: 99%

A visual approach towards forward collision warning for autonomous vehicles on Malaysian public roads

et al. 2022

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Background: Autonomous vehicles are important in smart transportation. Although exciting progress has been made, it remains challenging to design a safety mechanism for autonomous vehicles despite uncertainties and obstacles that occur dynamically on the road. Collision detection and avoidance are indispensable for a reliable decision-making module in autonomous driving. Methods: This study presents a robust approach for forward collision warning using vision data for autonomous vehicles on Malaysian public roads. The proposed architecture combines environment perception and lane localization to define a safe driving region for the ego vehicle. If potential risks are detected in the safe driving region, a warning will be triggered. The early warning is important to help avoid rear-end collision. Besides, an adaptive lane localization method that considers geometrical structure of the road is presented to deal with different road types. Results: Precision scores of mean average precision (mAP) 0.5, mAP 0.95 and recall of 0.14, 0.06979 and 0.6356 were found in this study. Conclusions: Experimental results have validated the effectiveness of the proposed approach under different lighting and environmental conditions.

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“…In this paper, the evaluation metrics used include precision, recall and mean average precision. 9 The source code used for the analysis can be found in the Software availability . 10 …”

Section: Methodsmentioning

confidence: 99%

A visual approach towards forward collision warning for autonomous vehicles on Malaysian public roads

et al. 2022

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“…Ultralytics Llc proposed YOLOv5s [16] in May 2020. YOLOv5s network can be roughly divided into four parts: input layer, backbone part, neck part, and output layer.…”

Section: A Improved Yolov5smentioning

confidence: 99%

Faster Light Detection Algorithm of Traffic Signs Based on YOLOv5s-A2

Kuerban

Chen

et al. 2023

IEEE Access

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Traffic sign recognition systems have been applied to advanced driving assistance and automatic driving systems to help drivers obtain important road information accurately. The current mainstream detection methods have high accuracy in this task, but the number of model parameters is large, and the detection speed is slow. Based on YOLOv5s as the basic framework, this paper proposes YOLOv5S-A2, which can improve the detection speed and reduce the model size at the cost of reducing the detection accuracy. Firstly, a data augmentation strategy is proposed by combining various operations to alleviate the problem of unbalanced class instances. Secondly, we proposed a path aggregation module of Feature Pyramid Network (FPN) to make new horizontal connections. It can enhance multi-scale feature representation capability and compensate for the loss of feature information. Thirdly, an attention detection head module is proposed to solve the aliasing effect in cross-scale fusion and enhance the representation of predictive features. Experiments on Tsinghua-Tencent 100K dataset (TT100K) show that our method can achieve more remarkable performance improvement and faster inference speed than other advanced technologies. Our method achieves 87.3% mean average precision (mAP) surpassing the original model's 7.9%, and the frames per second (FPS) value is maintained at 87.7. To show generality, we tested it on the German Traffic Sign Detection Benchmark (GTSDB) without tuning and obtained an average precision of 94.1%, and the FPS value is maintained at about 105.3. In addition, the number of YOLOv5s-A2 parameters is about 7.9 M.

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“…On the other hand, the size of the weight file of YOLOv5 target detection network model is small, which is nearly 90% smaller than YOLOv4, indicating that YOLOv5 model is suitable for deployment in embedded devices to implement real-time detection. In summary, the advantages of YOLOv5 network are its high detection accuracy, lightweight characteristics, and fast detection speed at the same time (Fang et al 2021).…”

Section: Yolov5 Deep Networkmentioning

confidence: 99%

Real-Time Detection and Recognition of Railway Traffic Signals Using Deep Learning

Staino

Suwalka

Mitra

et al. 2022

J. Big Data Anal. Transp.

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Automated detection and recognition of traffic signals are of great significance in railway systems. Autonomous driving solutions are well established for urban rail transportation systems. Many metro lines in service worldwide have reached the highest grade of automation where the train is automatically operated without any staff on board. However, autonomous driving is still an open challenge for mainline trains, due to the complexity of the mainline environment. In this context, automated recognition of wayside signals can help to minimise the risk of human error owing to low visibility and fatigue. It represents a key step towards the fully autonomous train. In this article we present a deep learning based approach for the above task. The You Only Look Once (YOLOv5) is used for detection and recognition of wayside signals. A heuristic is used to recognise blinking states. We consider FRSign dataset, a large collection of over 100,000 images of traffic signals from some of the trains in French Railways. A distilled and cleaned version of the dataset curated by us is used for training. The trained network has low computational overhead and can recognise traffic signals in real time and under diverse field conditions. It has robust performance even for complex scenes having multiple signals and light sources, and in adverse circumstances such as rain and night environments. The refined version of the dataset is published as open for validation and further research and development.

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A Deployment Scheme of YOLOv5 with Inference Optimizations Based on the Triton Inference Server

Cited by 17 publications

References 11 publications

A visual approach towards forward collision warning for autonomous vehicles on Malaysian public roads

A visual approach towards forward collision warning for autonomous vehicles on Malaysian public roads

Faster Light Detection Algorithm of Traffic Signs Based on YOLOv5s-A2

Real-Time Detection and Recognition of Railway Traffic Signals Using Deep Learning

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