Infrared and Visible Image Object Detection via Focused Feature Enhancement and Cascaded Semantic Extension

Xiao, Xiaowu; Wang, Bo; Miao, Lingjuan; Li, Linhao; Zhou, Zhiqiang; Ma, Jinlei; Dong, Dezun

doi:10.3390/rs13132538

Cited by 13 publications

(3 citation statements)

References 41 publications

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“…The purpose is to allow the network to learn more mixed features of the infrared and visible images, thus allowing the network to adapt to both modes of features. So we take the module’s design for feature enhancement from [ 26 ] and improve it. The Fusion shuffle module we designed is shown in Figure 5 .…”

Section: Methodsmentioning

confidence: 99%

Dual-YOLO Architecture from Infrared and Visible Images for Object Detection

Bao

Cao

Hao

et al. 2023

Sensors

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With the development of infrared detection technology and the improvement of military remote sensing needs, infrared object detection networks with low false alarms and high detection accuracy have been a research focus. However, due to the lack of texture information, the false detection rate of infrared object detection is high, resulting in reduced object detection accuracy. To solve these problems, we propose an infrared object detection network named Dual-YOLO, which integrates visible image features. To ensure the speed of model detection, we choose the You Only Look Once v7 (YOLOv7) as the basic framework and design the infrared and visible images dual feature extraction channels. In addition, we develop attention fusion and fusion shuffle modules to reduce the detection error caused by redundant fusion feature information. Moreover, we introduce the Inception and SE modules to enhance the complementary characteristics of infrared and visible images. Furthermore, we design the fusion loss function to make the network converge fast during training. The experimental results show that the proposed Dual-YOLO network reaches 71.8% mean Average Precision (mAP) in the DroneVehicle remote sensing dataset and 73.2% mAP in the KAIST pedestrian dataset. The detection accuracy reaches 84.5% in the FLIR dataset. The proposed architecture is expected to be applied in the fields of military reconnaissance, unmanned driving, and public safety.

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Section: Methodsmentioning

confidence: 99%

Dual-YOLO Architecture from Infrared and Visible Images for Object Detection

Bao

Cao

Hao

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…A few regional proposal approaches include faster R-CNN [98], regional-fast convolutional network (R-FCN) [99], and region-CNN (R-CNN) [100]. On the other hand, the non-regional approach includes YOLO [101] and SSD [102][103][104]. These pedestrian detection approaches are the root of CNN, thus becoming the grade for pedestrian detection.…”

Section: Approachesmentioning

confidence: 99%

Deep Learning-Based Pedestrian Detection in Autonomous Vehicles: Substantial Issues and Challenges

et al. 2022

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In recent years, autonomous vehicles have become more and more popular due to their broad influence over society, as they increase passenger safety and convenience, lower fuel consumption, reduce traffic blockage and accidents, save costs, and enhance reliability. However, autonomous vehicles suffer from some functionality errors which need to be minimized before they are completely deployed onto main roads. Pedestrian detection is one of the most considerable tasks (functionality errors) in autonomous vehicles to prevent accidents. However, accurate pedestrian detection is a very challenging task due to the following issues: (i) occlusion and deformation and (ii) low-quality and multi-spectral images. Recently, deep learning (DL) technologies have exhibited great potential for addressing the aforementioned pedestrian detection issues in autonomous vehicles. This survey paper provides an overview of pedestrian detection issues and the recent advances made in addressing them with the help of DL techniques. Informative discussions and future research works are also presented, with the aim of offering insights to the readers and motivating new research directions.

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“…A difference maximum loss function is proposed in this paper. 30 Due to the low signal-to-noise ratio and low spatial resolution resulting in a severe lack of texture details for small infrared targets, infrared ocean ships detection still faces great challenges. Ye 31 built a CAA-YOLO to alleviate the problems.…”

Section: Related Workmentioning

confidence: 99%

CBAM-YOLOv5 for infrared image object detection

Hoang¹,

Ninh²,

Hai³

2022

Artificial Intelligence and Machine Learning in Defense Applications IV

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Identifying an object of interest in thermal images plays a vital role in several military and civilian applications. The deep learning approach has shown its superiority in object detection in various RGB datasets. However, regarding to thermal images, their low resolution and shortage of detail properties impose a huge challenge that hinders the accuracy. In this paper, we propose an improved version of YOLOv5 model to tackle this problem. Convolution Block Attention Module (CBAM) is integrated into traditional YOLOv5 for better representation of objects by focusing on important features and neglecting unnecessary ones. The Selective Kernel Network(SENet) is added to maximize the shallow features usage. Furthermore, the multiscale detection mechanism is utilized to improve small object detection accuracy. We train our model on the mixed visible-thermal images collected from LSOTB-TIR, LLVIP, and COCO datasets. We evaluate the performance of our method on 8 classes of objects: person, bicycle, airplane, helicopter, car, motorbike, boat, and tank. Experiment results show that our approach can achieve mAP up to 90.2%, which outperforms the original YOLOv5 and other popular methods.

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Infrared and Visible Image Object Detection via Focused Feature Enhancement and Cascaded Semantic Extension

Cited by 13 publications

References 41 publications

Dual-YOLO Architecture from Infrared and Visible Images for Object Detection

Dual-YOLO Architecture from Infrared and Visible Images for Object Detection

Deep Learning-Based Pedestrian Detection in Autonomous Vehicles: Substantial Issues and Challenges

CBAM-YOLOv5 for infrared image object detection

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