Pedestrian Detection at Night Based on Faster R-CNN and Far Infrared Images

Galarza-Bravo, Michelle A.; Flores-Calero, Marco

doi:10.1007/978-3-319-97589-4_28

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…In the realm of nighttime pedestrian recognition, Ref. [150] introduces an enhanced Faster R-CNN architecture, which delivers significant improvements, particularly for distant pedestrians. The study in [151] tackles the issue of optical camera-based pedestrian recognition under adverse weather conditions by employing a cascaded classification technique that incorporates both local and global features, resulting in increased detection rates.…”

Section: Approaches For Pedestrian Detectionmentioning

confidence: 99%

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

Tahir,

Zhang,

Asim

et al. 2024

Algorithms

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Enhancing the environmental perception of autonomous vehicles (AVs) in intelligent transportation systems requires computer vision technology to be effective in detecting objects and obstacles, particularly in adverse weather conditions. Adverse weather circumstances present serious difficulties for object-detecting systems, which are essential to contemporary safety procedures, infrastructure for monitoring, and intelligent transportation. AVs primarily depend on image processing algorithms that utilize a wide range of onboard visual sensors for guidance and decisionmaking. Ensuring the consistent identification of critical elements such as vehicles, pedestrians, and road lanes, even in adverse weather, is a paramount objective. This paper not only provides a comprehensive review of the literature on object detection (OD) under adverse weather conditions but also delves into the ever-evolving realm of the architecture of AVs, challenges for automated vehicles in adverse weather, the basic structure of OD, and explores the landscape of traditional and deep learning (DL) approaches for OD within the realm of AVs. These approaches are essential for advancing the capabilities of AVs in recognizing and responding to objects in their surroundings. This paper further investigates previous research that has employed both traditional and DL methodologies for the detection of vehicles, pedestrians, and road lanes, effectively linking these approaches with the evolving field of AVs. Moreover, this paper offers an in-depth analysis of the datasets commonly employed in AV research, with a specific focus on the detection of key elements in various environmental conditions, and then summarizes the evaluation matrix. We expect that this review paper will help scholars to gain a better understanding of this area of research.

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Section: Approaches For Pedestrian Detectionmentioning

confidence: 99%

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

Tahir,

Zhang,

Asim

et al. 2024

Algorithms

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“…Michelle et al used two region of interest (ROI), pooling at the middle and bottom layers of the feature exactor, to obtain information at different scales. The results showed that this technique could effectively reduce the miss rate of pedestrian detection [12]. However, in Faster R-CNN, the region proposal network (RPN) only uses the last layer of the feature extraction network to generate proposals, which is not suitable for multi-scale object detection.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Object Detection in Infrared Streetscape Images Based on Deep Learning and Instance Level Data Augmentation

Zhang

et al. 2019

Applied Sciences

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The development of object detection in infrared images has attracted more attention in recent years. However, there are few studies on multi-scale object detection in infrared street scene images. Additionally, the lack of high-quality infrared datasets hinders research into such algorithms. In order to solve these issues, we firstly make a series of modifications based on Faster Region-Convolutional Neural Network (R-CNN). In this paper, a double-layer region proposal network (RPN) is proposed to predict proposals of different scales on both fine and coarse feature maps. Secondly, a multi-scale pooling module is introduced into the backbone of the network to explore the response of objects on different scales. Furthermore, the inception4 module and the position sensitive region of interest (ROI) align (PSalign) pooling layer are utilized to explore richer features of the objects. Thirdly, this paper proposes instance level data augmentation, which takes into account the imbalance between categories while enlarging dataset. In the training stage, the online hard example mining method is utilized to further improve the robustness of the algorithm in complex environments. The experimental results show that, compared with baseline, our detection method has state-of-the-art performance.

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SAYNet: Self-attention YOLO Network for Human Detection in Thermal and Infrared Images

Hazra,

Poddar,

Bhattacharjee

et al. 2024

Proceedings of 4th International Conference on Frontiers in Computing and Systems

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Pedestrian Detection at Night Based on Faster R-CNN and Far Infrared Images

Cited by 7 publications

References 29 publications

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

Multiscale Object Detection in Infrared Streetscape Images Based on Deep Learning and Instance Level Data Augmentation

SAYNet: Self-attention YOLO Network for Human Detection in Thermal and Infrared Images

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