YOLO-CIR: The network based on YOLO and ConvNeXt for infrared object detection

Zhou, Junhu; Zhang, Baohui; Yuan, Xilin; Lü, Cheng; Ji, Li; Zhang, Qian; Yue, Jiang

doi:10.1016/j.infrared.2023.104703

Cited by 24 publications

(7 citation statements)

References 32 publications

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“…Lai et al [25] added a channel attention mechanism into the segmentation task of parking slot detection. The authors designed a double-layer segmentation network where the upper layer takes up 1 4 size of the input image, resulting in an output of a coarse segmentation result for faster performance. This coarse segmentation result is cascaded into the lower segmentation network that takes a normal size input image and is responsible for a more detailed segmentation result.…”

Section: Image Segmentation Approachmentioning

confidence: 99%

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Review of Vision-Based Deep Learning Parking Slot Detection on Surround View Images

Wong

Goh

Connie

et al. 2023

Sensors

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Autonomous vehicles are gaining popularity, and the development of automatic parking systems is a fundamental requirement. Detecting the parking slots accurately is the first step towards achieving an automatic parking system. However, modern parking slots present various challenges for detection task due to their different shapes, colors, functionalities, and the influence of factors like lighting and obstacles. In this comprehensive review paper, we explore the realm of vision-based deep learning methods for parking slot detection. We categorize these methods into four main categories: object detection, image segmentation, regression, and graph neural network, and provide detailed explanations and insights into the unique features and strengths of each category. Additionally, we analyze the performance of these methods using three widely used datasets: the Tongji Parking-slot Dataset 2.0 (ps 2.0), Sejong National University (SNU) dataset, and panoramic surround view (PSV) dataset, which have played a crucial role in assessing advancements in parking slot detection. Finally, we summarize the findings of each method and outline future research directions in this field.

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Section: Image Segmentation Approachmentioning

confidence: 99%

“…Vision-based deep learning approaches have revolutionized various computer vision tasks, including object detection [ 1 , 2 ] and image segmentation [ 3 ]. They could also extend their application in the field of autonomous driving, where accurate perceptions of the surround view are crucial.…”

Section: Introductionmentioning

confidence: 99%

Review of Vision-Based Deep Learning Parking Slot Detection on Surround View Images

Wong

Goh

Connie

et al. 2023

Sensors

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“…Infrared thermal imaging uses signal processing, photoelectric conversion and other technical measures to convert infrared radiation in the target area into visual images 1 . It has the advantages of round-the-clock operation, good anti-jamming and not easy to be detected 2 , therefore, it is widely used in medical images 3 , industrial equipment 4 , security equipment 5 and military operations 6 .…”

Section: Introductionmentioning

confidence: 99%

“…The resolution of low-quality infrared images is usually lower than that of visible light images, and is often accompanied by problems such as susceptibility to noise carryover, blurred and missing edge details, which will greatly affect downstream tasks based on infrared images. For example, in IR graphics-based target detection tasks, these drawbacks greatly diminish the saliency of target features in the image and are not conducive to detecting targets with complex backgrounds 6 .…”

Section: Introductionmentioning

confidence: 99%

Dual-branch feature encoding framework for infrared images super-resolution reconstruction

Zhang,

Zhou,

Chen

2024

Sci Rep

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Infrared thermal imaging is a passive non-contact detection and identification technology, which is not subject to electromagnetic infection and good concealment, is widely used in military and commercial fields. However, due to the limitations of the existing infrared imaging system mechanisms, the spatial resolution of the acquired infrared images is low and the edge details are blurred, which in turn leads to poor performance in downstream missions based on infrared images. In this paper, in order to better solve the above problems, we propose a new super-resolution reconstruction framework for infrared images, called DBFE, which extracts and retains abundant structure and textual information for robust infrared image high-resolution reconstruction with a novel structure-textual encoder module. Extensive experiment demonstrates that our proposed method achieves significantly superior contraband high-resolution reconstruction results on the multiple dataset compared to progressive methods for high resolution infrared image reconstruction, effectively proving the practicability of the method proposed in this paper.

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“…In the field of machine vision, target detection is a highly challenging task with significant implications in practical scenarios. It plays a vital role in various applications, including automatic driving [4], visual monitoring [24,41,20], and navigation guidance [15,16]. The detection method needs to exhibit robustness under diverse lighting and environmental conditions, encompassing daytime, nighttime, rain, and fog.…”

Section: Introductionmentioning

confidence: 99%

Boosting Infrared Target Detection: Unveiling the Power of ResNet-SA and Detection Block

Lin

Yang

et al. 2023

Preprint

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We propose an IR-YOLOv5 to address the limitations of general target detection approaches in infrared scenes. Specifically, IR-YOLOv5 takes into account common issues present in infrared images, such as low resolution, high noise, low contrast, and small targets. To achieve an efficient interaction of information between different channels and obtain rich semantic information of targets in infrared images, the feature extraction network leverages the attention mechanism. Additionally, the detection scheme of YOLOv5 is enhanced, and a detection head designed for small and weak targets is incorporated to improve the detection probability of these targets in infrared images. Furthermore, the method proposes an efficient PANet target recognition network by designing the Detection Block module to effectively reduce the parameter size and ensure real-time operations of the detector while enhancing the detection accuracy. The proposed method is evaluated on the FLIR infrared autonomous driving dataset and outperforms yolov5 with a 4% improvement in mean Average Precision (mAP) and a reduced model parameter size.

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YOLO-CIR: The network based on YOLO and ConvNeXt for infrared object detection

Cited by 24 publications

References 32 publications

Review of Vision-Based Deep Learning Parking Slot Detection on Surround View Images

Review of Vision-Based Deep Learning Parking Slot Detection on Surround View Images

Dual-branch feature encoding framework for infrared images super-resolution reconstruction

Boosting Infrared Target Detection: Unveiling the Power of ResNet-SA and Detection Block

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