Vehicle detection in thermal images with an improved yolov3-tiny

Gong, Jing; Zhao, Jianhui; Fan, Li; Zhang, He

doi:10.1109/icpics50287.2020.9201995

Cited by 21 publications

(22 citation statements)

References 24 publications

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“…The reviewed AmI literature is good evidence of that. Although the works specifically identified along those lines [64,68,69,[77][78][79]82,85,96,102,105,106] cover only a limited subset of the broad spectrum of applications resulting from the technological progress in the field, they provide good intuition on the relevance of object detection as a key factor for making both vehicles and infrastructures safer, more efficient, comfortable and reliable. In particular, in-vehicle ITS systems [64,[77][78][79]85,96,102,105,106] (commonly known as Advanced Driving Assistance Systems or ADAS) stand out in the analysis as the central focus of interest.…”

Section: On-device Object Detection For Context Awareness In Ambient Intelligence Systemsmentioning

confidence: 99%

“…Although the works specifically identified along those lines [64,68,69,[77][78][79]82,85,96,102,105,106] cover only a limited subset of the broad spectrum of applications resulting from the technological progress in the field, they provide good intuition on the relevance of object detection as a key factor for making both vehicles and infrastructures safer, more efficient, comfortable and reliable. In particular, in-vehicle ITS systems [64,[77][78][79]85,96,102,105,106] (commonly known as Advanced Driving Assistance Systems or ADAS) stand out in the analysis as the central focus of interest. Such systems embed detection solutions conceived as safety mechanisms for monitoring both driver operations [77,78,85], preventing distractions, and ultimately, the loss of control of the vehicle, as well as on-road events [64,79,96,102,105,106], being the latter mainly implemented nowadays in the form of a warning instrument triggered in situations of potential collisions [79,96] or infractions [106], but also designed towards decision-making support in future autonomous vehicles [64,102,105].…”

Section: On-device Object Detection For Context Awareness In Ambient Intelligence Systemsmentioning

confidence: 99%

“…In particular, in-vehicle ITS systems [64,[77][78][79]85,96,102,105,106] (commonly known as Advanced Driving Assistance Systems or ADAS) stand out in the analysis as the central focus of interest. Such systems embed detection solutions conceived as safety mechanisms for monitoring both driver operations [77,78,85], preventing distractions, and ultimately, the loss of control of the vehicle, as well as on-road events [64,79,96,102,105,106], being the latter mainly implemented nowadays in the form of a warning instrument triggered in situations of potential collisions [79,96] or infractions [106], but also designed towards decision-making support in future autonomous vehicles [64,102,105]. For its part, regarding conventional ITS alternatives [68,69,82], the different approaches identified are aimed at road traffic control and early response to emergencies, applications in which the use of UAVs has been intensively explored [68,69] due to their capabilities to record and communicate information in a non-intrusive way, as well as to recognize areas inaccessible by other means.…”

Section: On-device Object Detection For Context Awareness In Ambient Intelligence Systemsmentioning

confidence: 99%

“…Such research has resulted in significantly more efficient detection frameworks, making them directly deployable on low-power and low-memory hardware platforms but, on the flip side, causing a dramatic accuracy degradation. Despite the latter, these novel models, e.g., Tiny YOLO [119,120] and SSDLite [118], are already considered standard benchmarking options when designing new lightweight detectors in AmI [50,58,64,69,74,84,89,92,93,96,97,[101][102][103][108][109][110].…”

Section: Architecturesmentioning

confidence: 99%

“…As reflected in the "Baseline" column of Table 2, leaving aside some few works [56,61,83,85] that have alternatively explored the creation of new architectures from scratch, the most common approach along those lines involves, however, using an existing architecture as a baseline and then either replacing the backbone [49,52,54,86] or introducing changes on the detector's architecture or the training process parameterization [55,58,62,69,84,89,90,93,96,98,99,101,104,108,110]. Such tweaks and their focus, as seen if the "Baseline" and "Enhancement Emphasis" columns are analyzed jointly, are closely related to the very nature of the detection framework adopted as a reference.…”

Section: Architecturesmentioning

confidence: 99%

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On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems

2021

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Ambient Intelligence (AmI) encompasses technological infrastructures capable of sensing data from environments and extracting high-level knowledge to detect or recognize users’ features and actions, as well as entities or events in their surroundings. Visual perception, particularly object detection, has become one of the most relevant enabling factors for this context-aware user-centered intelligence, being the cornerstone of relevant but complex tasks, such as object tracking or human action recognition. In this context, convolutional neural networks have proven to achieve state-of-the-art accuracy levels. However, they typically result in large and highly complex models that typically demand computation offloading onto remote cloud platforms. Such an approach has security- and latency-related limitations and may not be appropriate for some AmI use cases where the system response time must be as short as possible, and data privacy must be guaranteed. In the last few years, the on-device paradigm has emerged in response to those limitations, yielding more compact and efficient neural networks able to address inference directly on client machines, thus providing users with a smoother and better-tailored experience, with no need of sharing their data with an outsourced service. Framed in that novel paradigm, this work presents a review of the recent advances made along those lines in object detection, providing a comprehensive study of the most relevant lightweight CNN-based detection frameworks, discussing the most paradigmatic AmI domains where such an approach has been successfully applied, the different challenges arisen, the key strategies and techniques adopted to create visual solutions for image-based object classification and localization, as well as the most relevant factors to bear in mind when assessing or comparing those techniques, such as the evaluation metrics or the hardware setups used.

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