On the Role of Thermal Imaging in Automotive Applications: A Critical Review

Farooq, Muhammad Ali; Shariff, Waseem; O'Callaghan, David; Corcoran, Peter

doi:10.1109/access.2023.3255110

Cited by 8 publications

(5 citation statements)

References 112 publications

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“…In ADAS and AV systems, IR sensors can be efficiently used to generate precise and accurate perception of the surrounding environment by providing one or more cameras mounted in the vehicle. It is commonly used to generate object detection, classification and semantic segmentation [23].…”

Section: Role Of Infrared Sensors In Adas and Av Applicationsmentioning

confidence: 99%

“…In Table 3, a list of available datasets captured by various types of thermal sensors in different environmental conditions is provided. These datasets are widely used in building various pre-trained CNN models for several applications like Pedestrian detection, Vehicle detection and classification and small object detection in the thermal spectrum on GPU/edge-GPU devices for the automotive sensor suite [31]. Whereas there are inherent challenges also associated with the training and validation of the thermal data for CNN models are as follows: A limited number of publicly available datasets, little variability in the scenes such as weather, lighting and heat conditions, difficulties while using RGB pre-trained CNN models for thermal data.…”

Section: Survey On Deep Learning Approaches Using Infrared Images For...mentioning

confidence: 99%

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Latest Advancements in Perception Algorithms for ADAS and AV Systems Using Infrared Images and Deep Learning

Srinivasan,

Rajegowda,

Udhayakumar

2023

Digital Image Processing - Latest Advances and Applications

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Perception system plays an important role in Advanced driver assistance systems (ADAS) & Autonomous vehicles (AV) to understand the surrounding environment and further navigation. It is highly challenging to achieve the accurate perception of ego vehicle mimicking human vision. The available ADAS and AV solutions could able to perceive the environment to some extent using multiple sensors like Lidars, Radars and Cameras. National Highway Traffic Safety Administration Crash reports of ADAS and AV systems shows that the complete autonomy is challenging to achieve using the existing sensor suite. Particularly, in extreme weather, low light and night scenarios, there is a need for additional perception sensors. Infrared camera seems to be one of the potential sensors to address such extreme and corner cases. This chapter aimed to discuss the advantage of adding infrared sensors to perceive the environment accurately. The advancements in deep learning approaches further leverages to enhance ADAS features. Also, the limitations of current sensors, the need for infrared sensors and technology, artificial intelligence and current research focus using IR images are discussed in detail. Literature shows that by adding IR sensor to existing sensor suite may lead a way to achieve level 3 and above autonomous driving precisely.

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Section: Role Of Infrared Sensors In Adas and Av Applicationsmentioning

confidence: 99%

Section: Survey On Deep Learning Approaches Using Infrared Images For...mentioning

confidence: 99%

Latest Advancements in Perception Algorithms for ADAS and AV Systems Using Infrared Images and Deep Learning

Srinivasan,

Rajegowda,

Udhayakumar

2023

Digital Image Processing - Latest Advances and Applications

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“…Event-based vision for driver monitoring has many advantages including high temporal resolution, reduced motion blur, low latency, reduced power consumption for the onboard sensor suite, and most importantly privacy [22], [23], [9], [6], [2]. The following are some of the recent studies on driver monitoring using event cameras.…”

Section: B Event-based Driver Monitoring Systemmentioning

confidence: 99%

“…In recent years, the integration of computer vision technologies into the automotive industry has gained momentum, driven by goals of improved driver safety and enhanced human-vehicle interactions [1], [2]. Within driver monitoring system, detecting distractions holds pivotal importance due to its role in traffic accidents [3].…”

Section: Introductionmentioning

confidence: 99%

Neuromorphic Driver Monitoring Systems: A Computationally Efficient Proof-of-Concept for Driver Distraction Detection

Shariff,

Dilmaghani,

Kielty

et al. 2023

IEEE Open J. Veh. Technol.

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Driver Monitoring Systems (DMS) represent a promising approach for enhancing driver safety within vehicular technologies. This research explores the integration of neuromorphic event camera technology into DMS, offering faster and more localized detection of changes due to motion or lighting in an imaged scene. When applied to the observation of a human subject event camera provides a new level of sensing capabilities over conventional imaging systems. The study focuses on the application of DMS by incorporating the event cameras, augmented by submanifold sparse neural network models (SSNN) to reduce computational complexity. To validate the effectiveness of proposed machine learning pipeline built on event data we have opted the Driver Distraction as a critical use case. The SSNN model is trained on synthetic event data generated from the publicly available Drive&Act and Driver Monitoring Dataset (DMD) using a video-to-event conversion algorithm (V2E). The proposed approach yields comparable performance with state-of-the-art approaches, achieving an accuracy of 86.25% on the Drive&Act dataset and 80% on comprehensive DMD dataset while significantly reducing computational complexity. In addition, to demonstrate the generalization of our results the network is also evaluated using locally acquired event dataset gathered from a commercially available neuromorphic event sensor.

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“…With the ability to analyze the surrounding environment in realtime, these vehicles rely heavily on robust object detection algorithms to make informed and safe decisions [1], [2]. Although the prevailing source of information still constitute digital cameras, operating in visual spectrum, in the recent years the far infrared, so called thermovision cameras are gaining on importance [3]. In this paper we focus on this type of signals.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection of Small Objects in Automotive Thermal Images with Modern Deep Neural Architectures

Balon,

Knapik,

Cyganek

2023

Annals of Computer Science and Information Systems

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Thermal imaging has shown great potential for improving object detection in automotive settings, particularly in low light or adverse weather conditions. To help and further develop this industry, we extend our previously shared Thermal Automotive Dataset by more than 2000 new images and 2 novel object detecting models based on YOLOv5 and YOLOv7 architecture. We point how important is the size of the dataset. Additionally, we compare the performance of both models, to see which is more reliable and superior in terms of detecting small objects in thermal spectrum. Furthermore, we analysed how preprocessing affects thermal imaging dataset and models basing on it. The new dataset is available free from the Internet.

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On the Role of Thermal Imaging in Automotive Applications: A Critical Review

Cited by 8 publications

References 112 publications

Latest Advancements in Perception Algorithms for ADAS and AV Systems Using Infrared Images and Deep Learning

Latest Advancements in Perception Algorithms for ADAS and AV Systems Using Infrared Images and Deep Learning

Neuromorphic Driver Monitoring Systems: A Computationally Efficient Proof-of-Concept for Driver Distraction Detection

Real-Time Detection of Small Objects in Automotive Thermal Images with Modern Deep Neural Architectures

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