Intensity Inhomogeneity Suppressed Fuzzy C-Means for Infrared Pedestrian Segmentation

Wang, Yingfan; Bai, Xiangzhi

doi:10.1109/tits.2018.2875159

Cited by 11 publications

(4 citation statements)

References 31 publications

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“…For infrared object detection, the common infrared targets are infrared pedestrian detection [1,20,21,22], infrared vehicle and aircraft detection [3,4,5,23]. The main problem to be overcome is the lack of infrared data, followed by the problem of unclear infrared image features.…”

Section: A Infrared Object Detectionmentioning

confidence: 99%

Weak and Occluded Vehicle Detection in Complex Infrared Environment Based on Improved YOLOv4

Zhang

et al. 2021

IEEE Access

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Infrared small target detection is still a challenge in the field of object detection. At present, although there are many related research achievements, it surely needs further improvement. This paper introduced a new application of severely occluded vehicle detection in the complex wild background of weak infrared camera aerial images, in which more than 50% area of the vehicles are occluded. We used YOLOv4 as the detection model. By applying secondary transfer learning from visible dataset to infrared dataset, the model could gain a good average precision (AP). Firstly, we trained the model in the UCAS_AOD visible dataset, then, we transferred it to the VIVID visible dataset, finally we transferred the model to the VIVID infrared dataset for a second training. Meanwhile, added the hard negative example mining block to the YOLOv4 model, which could depress the disturbance of complex background thus further decrease the false detecting rate. Through experiments the average precision improved from90.34% to 91.92%, the F1 score improved from 87.5% to 87.98%, which demonstrated that the proposed algorithm generated satisfactory and competitive vehicle detection results. INDEX TERMS Infrared aerial image, occlusion, vehicle detection, hard negative example mining, YOLOv4.

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Section: A Infrared Object Detectionmentioning

confidence: 99%

Weak and Occluded Vehicle Detection in Complex Infrared Environment Based on Improved YOLOv4

Zhang

et al. 2021

IEEE Access

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“…Infrared Small Target Detection. Infrared object detection mainly contains infrared person detection [6,[13][14][15], infrared vehicle detection [7,16,17], infrared aircraft detection [5], and infrared creature recognition and counting [18]. Usually, the lack of an infrared dataset for training and the unclear infrared image features are the problems that need to be overcome.…”

Section: Related Workmentioning

confidence: 99%

FA‐YOLO: An Improved YOLO Model for Infrared Occlusion Object Detection under Confusing Background

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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Infrared target detection is a popular applied field in object detection as well as a challenge. This paper proposes the focus and attention mechanism-based YOLO (FA-YOLO), which is an improved method to detect the infrared occluded vehicles in the complex background of remote sensing images. Firstly, we use GAN to create infrared images from the visible datasets to make sufficient datasets for training as well as using transfer learning. Then, to mitigate the impact of the useless and complex background information, we propose the negative sample focusing mechanism to focus on the confusing negative sample training to depress the false positives and increase the detection precision. Finally, to enhance the features of the infrared small targets, we add the dilated convolutional block attention module (dilated CBAM) to the CSPdarknet53 in the YOLOv4 backbone. To verify the superiority of our model, we carefully select 318 infrared occluded vehicle images from the VIVID-infrared dataset for testing. The detection accuracy-mAP improves from 79.24% to 92.95%, and the F1 score improves from 77.92% to 88.13%, which demonstrates a significant improvement in infrared small occluded vehicle detection.

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“…The main reason is that the infrared ray will be affected by the atmospheric thermal radiation due to the distance between the target and the sensors in the scene is far away [4], [5]. In order to improve the contrast of infrared image and highlight the target details, image enhancement is an effective method [2], [4]. In Fig.1, we give examples of the original infrared images and their enhanced effects in different scenes.…”

Section: Introductionmentioning

confidence: 99%

“…It can be seen that the enhanced infrared images have higher contrast, clearer texture detail and more comfortable visual effect. Thus, image enhancement has attracted more and more attention from researchers [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Detail Equalization for Infrared Image Enhancement Based on Multi-Scale Convolution

Liu

Pan

2020

IEEE Access

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In order to solve the problem of low contrast and fuzzy detail in infrared image, we propose an infrared image enhancement method based on multi-scale and adaptive bi-interval histogram equalization with details. The whole image enhancement method mainly consists of four parts: details enhancement, contrast stretch, edge enhancement and reconstruction of enhancement images. Firstly, the multi-scale convolution is used to enhance the details of image; Secondly, taking maximize the variance between classes and minimize the variance as fitness function and solved the threshold of the infrared image by genetic algorithm, then dividing the infrared image into two sub-intervals according to the threshold. After that, the bi-interval histogram equalization with details is applied to enhance the global contrast, at the same time, using the mean square deviation and average gray equalization to improve the brightness of the image. Finally, the enhanced image by adaptive bi-interval histogram equalization with details and the image processed by adaptive limited Laplace operator are fused by linear weighting to reconstruct the final enhancement image. The experimental results show that the proposed method can outperform state-of-the-art ones in both qualitative and quantitative comparisons.

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Intensity Inhomogeneity Suppressed Fuzzy C-Means for Infrared Pedestrian Segmentation

Cited by 11 publications

References 31 publications

Weak and Occluded Vehicle Detection in Complex Infrared Environment Based on Improved YOLOv4

Weak and Occluded Vehicle Detection in Complex Infrared Environment Based on Improved YOLOv4

FA‐YOLO: An Improved YOLO Model for Infrared Occlusion Object Detection under Confusing Background

An Adaptive Detail Equalization for Infrared Image Enhancement Based on Multi-Scale Convolution

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