Multi-Scale YOLOv5-AFAM Based Infrared Dim Small Target Detection

Wang, Yuexing; Zhao, Liang; Ma, Yixiang; Shi, Yuanyuan; Tian, Jinwen

doi:10.20944/preprints202306.0281.v1

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…Moreover, to verify the superiority of the proposed network, we compare our study with the other four networks: Faster R-CNN, YOLOv5, FA-YOLO, 27 and YOLO-AFAM. 28 As shown in Table 6, the proposed vehicle detection method presents the best performance. We attribute it to the improvements of the proposed method.…”

Section: Evaluation Of Vehicle Detection Under Different Environmentsmentioning

confidence: 97%

“…For instance, Du et al 27 proposed the focus and attention mechanism-based YOLO (FA-YOLO) to detect the infrared occluded vehicles in the complex background of remote sensing images. Wang et al 28 proposed a multi-scale YOLOv5-AFAM (Adaptive Fusion Attention Module) algorithm to realize high-accuracy and real-time detection. Xue et al 25 designed a fast saliency map of vehicle targets in infrared images at night to achieve rapid acquisition of regions of interest (ROIs) for vehicles.…”

Section: Introductionmentioning

confidence: 99%

“…The current methods solve this problem by either mining the deep semantic information or expanding the confusing negative sample training to depress the false positives. 25,27,28 Unfortunately, these methods will inevitably lead to a large computational load.…”

Section: Introductionmentioning

confidence: 99%

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ComS-YOLO: a combinational and sparse network for detecting vehicles in aerial thermal infrared images

Zhang,

2024

J. Appl. Rem. Sens.

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Vehicle detection using aerial thermal infrared images has received significant attention because of its strong capability for day and night observations to supply information for vehicle tracking, traffic monitoring, and road network planning. Compared with aerial visible images, aerial thermal infrared images are not sensitive to lighting conditions. However, they have low contrast and blurred edges. Therefore, a combinational and sparse you-only-look-once (ComS-YOLO) neural network is put forward to accurately and quickly detect vehicles in aerial thermal infrared images. Therein, we adjust the structure of the deep neural network to balance the detection accuracy and running time. In addition, we propose an objective function that utilizes the diagonal distance of the corresponding minimum external rectangle, which prevents non-convergence when there is an inclusion relationship between the prediction and true boxes or in the case of width and height alignment. Furthermore, to avoid over-fitting in the training stage, we eliminate some redundant parameters via constraints and on-line pruning. Finally, experimental results on the NWPU VHR-10 and DARPA VIVID datasets show that the proposed ComS-YOLO network effectively and efficiently identifies the vehicles with a low missed rate and false detection rate. Compared with the Faster R-CNN and a series of YOLO neural networks, the proposed neural network presents satisfactory and competitive results in terms of the detection accuracy and running time. Furthermore, vehicle detection experiments under different environments are also carried out, which shows that our method can achieve an excellent and desired performance on detection accuracy and robustness of vehicle detection.

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Section: Evaluation Of Vehicle Detection Under Different Environmentsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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ComS-YOLO: a combinational and sparse network for detecting vehicles in aerial thermal infrared images

Zhang,

2024

J. Appl. Rem. Sens.

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Multi-Scale Target Detection in Autonomous Driving Scenarios Based on YOLOv5-AFAM

Ma,

Zhao,

Liu

et al. 2024

Applied Sciences

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Multi-scale object detection is critically important in complex driving environments within the field of autonomous driving. To enhance the detection accuracy of both small-scale and large-scale targets in complex autonomous driving environments, this paper proposes an improved YOLOv5-AFAM algorithm. Firstly, the Adaptive Fusion Attention Module (AFAM) and Down-sampling Module (DownC) are introduced to increase the detection precision of small targets. Secondly, the Efficient Multi-scale Attention Module (EMA) is incorporated, enabling the model to simultaneously recognize small-scale and large-scale targets. Finally, a Minimum Point Distance IoU-based Loss Function (MPDIou-LOSS) is introduced to improve the accuracy and efficiency of object detection. Experimental validation on the KITTI dataset shows that, compared to the baseline model, the improved algorithm increased precision by 2.4%, recall by 2.6%, mAP50 by 1.5%, and mAP50-90 by an impressive 4.8%.

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Research on Infrared Dim Target Detection Based on Improved YOLOv8

Liu,

Li,

Cao

et al. 2024

Remote Sensing

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Addressing the formidable challenges in spatial infrared dim target detection, this paper introduces an advanced detection approach based on the refinement of the YOLOv8 algorithm. In contrast to the conventional YOLOv8, our method achieves remarkable improvements in detection accuracy through several novel strategies. Notably, by incorporating a deformable convolutional module into the YOLOv8 backbone network, our method effectively captures more intricate image features, laying a solid foundation for subsequent feature fusion and detection head predictions. Furthermore, a dedicated small target detection layer, built upon the original model, significantly enhances the model’s capability in recognizing infrared small targets, thereby boosting overall detection performance. Additionally, we utilize the WIoU-v3 as the localization regression loss function, effectively reducing sensitivity to positional errors and leveraging the advantages of multi-attention mechanisms. To enrich the quantity and quality of the spatial infrared dim target dataset, we employ image enhancement techniques to augment the original dataset. Extensive experiments demonstrate the exceptional performance of our method. Specifically, our approach achieves a precision of 95.6%, a recall rate of 94.7%, and a mean average precision (mAP) exceeding 97.4%, representing substantial improvements over the traditional YOLOv8 algorithm. Moreover, our detection speed reaches 59 frames/s, satisfying the requirements for real-time detection. This achievement not only validates the efficacy and superiority of our algorithm in spatial infrared dim target detection, but also offers novel insights and methodologies for research and applications in related fields, holding immense potential for future applications.

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Multi-Scale YOLOv5-AFAM Based Infrared Dim Small Target Detection

Cited by 3 publications

References 24 publications

ComS-YOLO: a combinational and sparse network for detecting vehicles in aerial thermal infrared images

ComS-YOLO: a combinational and sparse network for detecting vehicles in aerial thermal infrared images

Multi-Scale Target Detection in Autonomous Driving Scenarios Based on YOLOv5-AFAM

Research on Infrared Dim Target Detection Based on Improved YOLOv8

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