Survey of the Image Recognition Based on Deep Learning Network for Autonomous Driving Car

Liu, Jinqiao

doi:10.1109/isctt51595.2020.00007

Cited by 5 publications

(7 citation statements)

References 4 publications

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“…Techniques such as augmenting of architecture such as layers, using of normalization methods such as batch, L2 and use of dropouts, etc can help in optimizing and customizing a neural network. The effect these techniques can be found in the studies [7][8][9][10].…”

Section: Related Workmentioning

confidence: 99%

“…Hence, when using these neural nets, we need to optimize and customize them so that we can make the architecture simple, memory space less, not dependent on huge GPUs and make latency less. Of course, when such kind of optimization is done tradeoffs occur such as scale and complexity of the application will decrease but as long as the accuracy of the optimized model is within acceptable ranges, we can say the optimization is successful [7,8].…”

Section: Basic Workingmentioning

confidence: 99%

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Optimization of Deep Neural Networks for Enhanced Efficiency in Small Scale Autonomous Vehicles

Urooj,

Dar,

Mehfuz

et al. 2024

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Autonomous vehicles of the contemporary era constitute a sophisticated blend of artificial intelligence and electronic components. These vehicles operate autonomously by employing neural networks trained to interpret visual input from multiple onboard cameras and subsequently produce corresponding steering angles. However, the existing neural networks are characterized by their substantial scale, necessitating substantial GPU resources, and are prone to latency issues and complex architectural requirements. These factors render these networks unsuitable for small-scale applications where latency, complex architecture, and expensive hardware are prohibitive. This paper proposes a methodology for optimizing these neural networks for small-scale operations while preserving their accuracy and precision. This is achieved through a fine-tuning process that customizes the architecture and modifies various functional values and their parameters, resulting in a deep neural network tailored for small-scale applications. This optimized network boasts a simpler architecture, lower storage requirements, and reduced demand for GPU resources. The network is developed, trained, and evaluated using TensorFlow, a widely employed API for machine learning applications. The optimized network offers several advantages, including reduced latency, a customizable architecture, minimized memory requirements, and decreased GPU demand, making it a viable solution for various applications. The paper provides a detailed exploration of the development of this bespoke deep neural network and its potential implications for the future of small-scale autonomous vehicles.

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Section: Related Workmentioning

confidence: 99%

Section: Basic Workingmentioning

confidence: 99%

Optimization of Deep Neural Networks for Enhanced Efficiency in Small Scale Autonomous Vehicles

Urooj,

Dar,

Mehfuz

et al. 2024

View full text Add to dashboard Cite

show abstract

“…Image recognition in intelligent driving is divided into active and passive image recognition [8]. Simply put, active image recognition is image recognition that obtains the required information through user guidance or interaction, while passive image recognition is the automated recognition and analysis of images.…”

Section: Intelligent Drivingmentioning

confidence: 99%

“…FlowNet framework plays a vital role in intelligent driving. Through binocular vision depth perception and motion estimation, it has realized vital functions such as 3D scene perception, distance calculation and collision warning, visual odometer, obstacle detection and tracking, and attitude estimation, further improving the perception and decision-making ability of the auto drive system [8]. In 2020, Debrina Dutta et al…”

Section: Intelligent Drivingmentioning

confidence: 99%

The Application and Development of Image Recognition Technology in the Field of Computer Vision

Luo

2024

HSET

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Image recognition is an important field of computer vision research. With the emergence of convolutional neural networks, image recognition has rapidly developed in many fields and has entered people's daily lives. This article reviews image recognition applications and future prospects in three representative fields: healthcare, intelligent driving, and security monitoring. In medical image analysis, image recognition technology can improve medical image interpretation speed and accuracy by automatically learning image features, providing doctors with more comprehensive information and decision-making support. Regarding intelligent driving, image recognition can help vehicles perceive and understand the traffic environment in real time, improving driving safety and automation level. In terms of security monitoring, image recognition plays a vital role in facial recognition, object detection, and behavior analysis, providing intelligent monitoring and security guarantees. However, these areas still have challenges, such as large data volumes, labeling difficulties, and privacy issues. In the future, people can look forward to combining image recognition technology with other technologies to improve models' interpretability and decision-making ability and further promote the development of these fields.

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“…The purpose of object detection requires the computer to automatically identify the object from the original images concerning its category, position, and confidence. The developments of object detection have also achieved significant milestones in various domains such as autonomous driving [5,6], robot vision [7], video surveillance [8,9], and medical imaging [10]. However, in contrast to the scenarios mentioned above, deep learning has still been underappreciated in ocean exploitation [11].…”

Section: Introductionmentioning

confidence: 99%

Multi-Site and Multi-Scale Unbalanced Ship Detection Based on CenterNet

Zhang

Hou

2022

Electronics

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Object detection plays an essential role in the computer vision domain, especially the machine learning-based approach, which has developed rapidly in the past decades. However, the development of convolutional neural networks in the marine field is relatively slow, such as in ship classification and tracking. In this paper, ship detection is considered as a central point classification and regression task but discards the non-maximum suppression operation. We first improved the deep layer aggregation network to enhance the feature extraction capability of tiny targets, then reduced the number of parameters through the lightweight convolution module, and finally employed a unique activation function to enhance the nonlinearity of the model. By doing this, the improved network not only suits unbalanced sample ratios in classifying, but is more robust in scenarios where both the number and resolution of samples are unstable. Experimental results demonstrate that the proposed approach obtains outstanding performance and especially suits tiny object detection compared with current advanced methods. Furthermore, in contrast to the original CenterNet framework, the mAP of the proposed approach increased by 5.6%.

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Survey of the Image Recognition Based on Deep Learning Network for Autonomous Driving Car

Cited by 5 publications

References 4 publications

Optimization of Deep Neural Networks for Enhanced Efficiency in Small Scale Autonomous Vehicles

Optimization of Deep Neural Networks for Enhanced Efficiency in Small Scale Autonomous Vehicles

The Application and Development of Image Recognition Technology in the Field of Computer Vision

Multi-Site and Multi-Scale Unbalanced Ship Detection Based on CenterNet

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