Bidirectional parallel multi-branch convolution feature pyramid network for target detection in aerial images of swarm UAVs

Fu, Lijun; Gu, Wentao; Li, Wei; Chen, Liang; Ai, Yongbao; Wang, Hualei

doi:10.1016/j.dt.2020.09.018

Cited by 15 publications

(4 citation statements)

References 11 publications

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“…Wei et al 30 aimed at the problem of scale change in UAV aerial images and introduced residual network and skip connection mechanism based on the single-shot multibox detector 31 to reduce feature redundancy and reduce the risk of gradient disappearance in the model. Fu et al 32 developed the single-stage detector bidirectional parallel multibranch feature pyramid network for UAV airto-ground object detection. They used a bidirectional parallel multibranch convolution module to create a feature pyramid and strengthen the model's multiscale feature representation ability.…”

Section: Object Detection Algorithms In Aerial Imagesmentioning

confidence: 99%

AIOD-YOLO: an algorithm for object detection in low-altitude aerial images

Yan,

Liu,

Lyu

et al. 2024

J. Electron. Imag.

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Aerial image object detection has a wide range of application values in civilian or military fields. Due to its unique high-altitude imaging viewpoint and the multiangle shooting method, aerial images lead to problems, such as small objects being detected in the image, large variations in object scales, and dense distribution. To alleviate the above problems, we propose an improved aerial image object detection algorithm aerial images object detection based on YOLO (AIOD-YOLO) based on YOLOv8-s. First, we propose the multibranch contextual information aggregation module. It enhances the network's perception of small objects by associating object information with the surrounding environment, thereby compensating for the lack of feature information for small objects. In addition, we propose the multilayer feature cascade efficient aggregation network, which leverages multigradient flow fusion of features at different scales. This approach aids the network in capturing a wide range of scale information and effectively mitigates the issue of missed detections caused by variations in object scales. Finally, we propose the adaptive task alignment label assignment strategy to address the issue of dense object distribution. The strategy incorporates the cosine similarity calculation to assess alignment globally and simultaneously adjusts the weights of positive and negative samples. We optimize the precision of label assignment for dense objects in aerial images, effectively resolving the challenges posed by their close proximity. The experiments on the VisDrone dataset demonstrate that AIOD-YOLO achieves a significant 7.2% improvement in mAP compared to the baseline model YOLOv8-s. The mAP0.5 of AIOD-YOLO is also improved by 14.1%, 7.9%, and 7.5% on SeaDronesSee v2, AI-TOD, and TinyPerson datasets, respectively, which validates the generalization of our proposed algorithm. AIOD-YOLO offers a superior information processing approach for tasks related to aerial image object detection in both civilian and military applications.

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Section: Object Detection Algorithms In Aerial Imagesmentioning

confidence: 99%

AIOD-YOLO: an algorithm for object detection in low-altitude aerial images

Yan,

Liu,

Lyu

et al. 2024

J. Electron. Imag.

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“…In order to fully solve this problem, significant research work on intelligent detection technology has been carried out. For example, considering the task of the real-time ground multi-scale object detection of clustered UAVs, one article [9] has improved the basis of the bidirectional parallel multi-branch feature pyramid network (BPMFPN) and proposed a new detection model called BPMFPN. This model strengthens the expression ability of each scale feature layer in aerial images by constructing a bidirectional parallel pyramid network, then integrates the detection network into a single detector.…”

Section: Introductionmentioning

confidence: 99%

ATS-YOLOv7: A Real-Time Multi-Scale Object Detection Method for UAV Aerial Images Based on Improved YOLOv7

Zhang,

Shao,

et al. 2023

Electronics

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The objects in UAV aerial images have multiple scales, dense distribution, and occlusion, posing considerable challenges for object detection. In order to address this problem, this paper proposes a real-time multi-scale object detection method based on an improved YOLOv7 model (ATS-YOLOv7) for UAV aerial images. First, this paper introduces a feature pyramid network, AF-FPN, which is composed of an adaptive attention module (AAM) and a feature enhancement module (FEM). AF-FPN reduces the loss of deep feature information due to the reduction of feature channels in the convolution process through the AAM and FEM, strengthens the feature perception ability, and improves the detection speed and accuracy for multi-scale objects. Second, we add a prediction head based on a transformer encoder block on the basis of the three-head structure of YOLOv7, improving the ability of the model to capture global information and feature expression, thus achieving efficient detection of objects with tiny scales and dense occlusion. Moreover, as the location loss function of YOLOv7, CIoU (complete intersection over union), cannot facilitate the regression of the prediction box angle to the ground truth box—resulting in a slow convergence rate during model training—this paper proposes a loss function with angle regression, SIoU (soft intersection over union), in order to accelerate the convergence rate during model training. Finally, a series of comparative experiments are carried out on the DIOR dataset. The results indicate that ATS-YOLOv7 has the best detection accuracy (mAP of 87%) and meets the real-time requirements of image processing (detection speed of 94.2 FPS).

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“…However, the images captured by UAV are different from other images in many aspects. The difficulties of data annotation, the diversity of targets, the complexity of the background, and the poor image quality lead to the poor detection effect of the traditional lightweight network model on the images captured by UAV 4 . According to the above development status and difficulties of target detection based on UAV, a lightweight real-time detection model is designed to solve the related problems of target detection to let UAV fully applications in various scenarios.…”

Section: Introductionmentioning

confidence: 99%

Lightweight multi-target detection algorithm for unmanned aerial vehicle aerial imagery

Liu,

Ma,

Wang

2023

J. Appl. Rem. Sens.

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.Compared with the image captured in the natural scene, the image obtained by unmanned aerial vehicle (UAV) aerial photography has a more complex background and many dense small targets, which puts forward higher requirements for the detection accuracy of the target detection algorithm. However, because the UAV is a kind of small mobile device, how to ensure its real-time detection effect has been a problem. Aiming at these problems, the lightweight YOLOv7 algorithm, namely LRT-YOLOv7, is designed. First, the enhance feature fusion module and the transformer efficient layer aggregation networks module are proposed to improve the performance of feature extraction and fusion to enhance the efficiency of small target detection. Second, aiming at the problems of small target size and complex background in the UAV images, the detection head structure is redesigned in the YOLOv7-tiny algorithm to enhance the multi-scale feature fusion ability of the algorithm and thereby improve the algorithm’s detection accuracy for small targets. Finally, ablation, comparison, and visualization validation experiments were conducted using precision, recall, mean average precision, and frames per second (FPS) as evaluation indicators. The results show that the detection speed of the LRT-YOLOv7 algorithm on the self-made traffic target dataset is 133.8 FPS, and the precision indicator is 84.58%. Therefore, the LRT-YOLOv7 algorithm has high accuracy and real-time performance in traffic target detection tasks for UAV aerial imagery.

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Bidirectional parallel multi-branch convolution feature pyramid network for target detection in aerial images of swarm UAVs

Cited by 15 publications

References 11 publications

AIOD-YOLO: an algorithm for object detection in low-altitude aerial images

AIOD-YOLO: an algorithm for object detection in low-altitude aerial images

ATS-YOLOv7: A Real-Time Multi-Scale Object Detection Method for UAV Aerial Images Based on Improved YOLOv7

Lightweight multi-target detection algorithm for unmanned aerial vehicle aerial imagery

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