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

Du, Shuangjiang; Zhang, Baofu; Pin, Zhang; Peng, Xiang; Xue, Hong Yan

doi:10.1155/2021/1896029

Cited by 26 publications

(11 citation statements)

References 32 publications

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“…Moreover, to enhance the features of the infrared small targets, Du added the dilated convolutional block attention module (dilated CBAM) 18 to the CSPdarknet53 in the YOLOv4 15 backbone, called FA-YOLO. 28 This approach achieved a considerable improvement in infrared small occluded vehicle detection. Dai 29 presented an SSD-like object detection method TIRNet.…”

Section: Related Workmentioning

confidence: 97%

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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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Section: Related Workmentioning

confidence: 97%

“…These methods achieved a better performance for night-time object detection in different fields, such as vehicle detection 28 and autonomous driving. 29 Despite the recent progress, it was difficult to transplant these models to mobile devices after training, especially for drone equipment, satellite equipment, infrared cameras, etc.…”

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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“…Squeeze-and-excitation [38] is a well-known channel attention mechanism that allows the model to learn the importance of different channel characteristics. Later works, the convolutional block attention module (CBAM) [45]- [48] combines spatial and channel attention mechanism modules, using convolution to compute spatial attention. However, convolution can only capture local relations, but not the longrange dependencies required for object detection.…”

Section: Attention Mechanismmentioning

confidence: 99%

An Improved Algorithm for Wind Turbine Blade Defect Detection

et al. 2022

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With the increase in wind power generation, wind turbine blades require regular inspections to ensure they continue to operate safely. You only look once (YOLO) is one of the most widely used object detection algorithms and is easy to deploy into drone devices. To enhance the real-time detection of small target defects in wind turbine blades, this paper proposes an improved attention and feature balanced YOLO (AFB-YOLO) algorithm based on YOLOv5s. Specifically, AFB-YOLO improves the feature pyramid network by using weighted feature fusion and cross-scale connections. The improved feature pyramid network solves the problem that most previous feature pyramid networks treat all input features equally, and obtains more feature information. Furthermore, the coordinate attention (CA) module is introduced into the network to augment the representations of the objects of interest. Finally, the paper redesigned the loss function through efficient intersection over union (EIoU) loss to make the model obtain a better localization effect. The experimental results on the imagery of wind turbine blade defects indicate that our method shows significant gains in performance. The mean average precision (mAP50) of AFB-YOLO is 83.7% and the detection accuracy is improved by 4.0% compared to the original YOLOv5s model. The experiments in this paper demonstrate that AFB-YOLO is more effective and robust than state-of-theart detectors.INDEX TERMS wind turbine blade, YOLOv5s, feature fusion, coordinate attention, EIoU loss.

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“…Dai et al [ 37 ] put forward a novel object detection approach, termed TIRNet, where the residual branch was introduced to get robust and discriminating features for accurate box regression and classification. Du et al [ 38 ] proposed FA-YOLO with a CBAM module in the backbone to enhance the performance of infrared occlusion object detection under a confusing background. In terms of improving feature fusion strategy, Dai et al [ 39 ] proposed the asymmetric contextual modulation (ACM), which explored the fusion method between deep and shallow features.…”

Section: Related Workmentioning

confidence: 99%

CAA-YOLO: Combined-Attention-Augmented YOLO for Infrared Ocean Ships Detection

Yuan

Qian

et al. 2022

Sensors

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Infrared ocean ships detection still faces great challenges due to the low signal-to-noise ratio and low spatial resolution resulting in a severe lack of texture details for small infrared targets, as well as the distribution of the extremely multiscale ships. In this paper, we propose a CAA-YOLO to alleviate the problems. In this study, to highlight and preserve features of small targets, we apply a high-resolution feature layer (P2) to better use shallow details and the location information. In order to suppress the shallow noise of the P2 layer and further enhance the feature extraction capability, we introduce a TA module into the backbone. Moreover, we design a new feature fusion method to capture the long-range contextual information of small targets and propose a combined attention mechanism to enhance the ability of the feature fusion while suppressing the noise interference caused by the shallow feature layers. We conduct a detailed study of the algorithm based on a marine infrared dataset to verify the effectiveness of our algorithm, in which the AP and AR of small targets increase by 5.63% and 9.01%, respectively, and the mAP increases by 3.4% compared to that of YOLOv5.

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

Cited by 26 publications

References 32 publications

CBAM-YOLOv5 for infrared image object detection

CBAM-YOLOv5 for infrared image object detection

An Improved Algorithm for Wind Turbine Blade Defect Detection

CAA-YOLO: Combined-Attention-Augmented YOLO for Infrared Ocean Ships Detection

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