Contour Information-Guided Multi-Scale Feature Detection Method for Visible-Infrared Pedestrian Detection

Xu, Xiaoyu; Zhan, Weida; Zhu, Daming; Jiang, Yichun; Chen, Yu; Guo, Jinghong

doi:10.3390/e25071022

Cited by 3 publications

(2 citation statements)

References 49 publications

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“…A multi-scale fusion of various prior features was used in [38] to enhance underwater images and to facilitate subsequent visual tasks for the capture of underwater scenes. A preprocessing method was proposed in [39] to suppress background interference for infrared pedestrian object detection.…”

Section: Image Preprocessing For Computer Visionmentioning

confidence: 99%

Image Sampling Based on Dominant Color Component for Computer Vision

Wang

Cui

et al. 2023

Electronics

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Image sampling is a fundamental technique for image compression, which greatly improves the efficiency of image storage, transmission, and applications. However, existing sampling algorithms primarily consider human visual perception and discard irrelevant information based on subjective preferences. Unfortunately, these methods may not adequately meet the demands of computer vision tasks and can even lead to redundancy because of the different preferences between human and computer. To tackle this issue, this paper investigates the key features of computer vision. Based on our findings, we propose an image sampling method based on the dominant color component (ISDCC). In this method, we utilize a grayscale image to preserve the essential structural information for computer vision. Then, we construct a concise color feature map based on the dominant channel of pixels. This approach provides relevant color information for computer vision tasks. We conducted experimental evaluations using well-known benchmark datasets. The results demonstrate that ISDCC adapts effectively to computer vision requirements, significantly reducing the amount of data needed. Furthermore, our method has a minimal impact on the performance of mainstream computer vision algorithms across various tasks. Compared to other sampling approaches, our proposed method exhibits clear advantages by achieving superior results with less data usage.

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Section: Image Preprocessing For Computer Visionmentioning

confidence: 99%

Image Sampling Based on Dominant Color Component for Computer Vision

Wang

Cui

et al. 2023

Electronics

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show abstract

“…To address the effects of occlusion and overlap on pedestrian detection, most previous studies extracted feature information by thresholding subtraction in the background. Xu [9] et al…”

Section: Introductionmentioning

confidence: 99%

An improved YOLO algorithm with multisensing for pedestrian detection

Gong,

Wang,

Huang

et al. 2024

SIViP

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Although pedestrian detection techniques are improving, this task is still challenging due to the problems of target occlusion, small targets, and complex pedestrian backgrounds in images of different scenes. As a result, the You Only Look Once (YOLO) algorithm exhibits lower detection accuracy. In this paper, the use of multiple dilated convolutions to sample feature images is proposed avoid the information loss incurred repeated sampling, to improve the feature extraction and target detection performance of the algorithm.In addition, a lightweight shuffle-based efficient channel attention (SECA) mechanism is introduced to conduct grouping in the channel dimension and perform parallel processing for each subfeature map channel. A new branch is introduced to enrich the channel feature information for multiscale feature representation. Finally, a distance intersection over union-based nonmaximum suppression (DIoU-NMS) method is introduced to minimize the occurrence of missed targets due to occlusion by taking the prediction box and ground truth box centroid locations information into account without increasing the computational cost over that of normal NMS. Our method is extensively evaluated on several challenging pedestrian detection datasets, achieving 87.73%, 34.7%, 93.96% and 95.23% mean average precision (mAP) values on PASCAL VOC 2012, MS COCO, Caltech Pedestrian and INRIA Person, which are respectively. The experimental results demonstrate the effectiveness of the method.

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An improved YOLO algorithm with multisensing for pedestrian detection

Gong,

Wang,

Huang

et al. 2024

Preprint

View full text Add to dashboard Cite

Although pedestrian detection techniques are improving, this task is still challenging due to the problems of target occlusion, small targets, and complex pedestrian backgrounds in images of different scenes. As a result, the You Only Look Once (YOLO) algorithm exhibits lower detection accuracy. In this paper, the use of multiple dilated convolutions to sample feature images is proposed avoid the information loss incurred repeated sampling, to improve the feature extraction and target detection performance of the algorithm. In addition, a lightweight shuffle-based efficient channel attention (SECA) mechanism is introduced to conduct grouping in the channel dimension and perform parallel processing for each subfeature map channel. A new branch is introduced to enrich the channel feature information for multiscale feature representation. Finally, a distance intersection over union-based nonmaximum suppression (DIoU-NMS) method is introduced to minimize the occurrence of missed targets due to occlusion by taking the prediction box and ground truth box centroid locations information into account without increasing the computational cost over that of normal NMS. Our method is extensively evaluated on several challenging pedestrian detection datasets, achieving 87.73%, 34.7%, 93.96% and 95.23% mean average precision (mAP) values on PASCAL VOC 2012, MS COCO, Caltech Pedestrian and INRIA Person, which are respectively. The experimental results demonstrate the effectiveness of the method.

show abstract

Contour Information-Guided Multi-Scale Feature Detection Method for Visible-Infrared Pedestrian Detection

Cited by 3 publications

References 49 publications

Image Sampling Based on Dominant Color Component for Computer Vision

Image Sampling Based on Dominant Color Component for Computer Vision

An improved YOLO algorithm with multisensing for pedestrian detection

An improved YOLO algorithm with multisensing for pedestrian detection

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