Bidirectional parallel multi-branch convolution feature pyramid network for target detection in aerial images of swarm UAVs
| Autor: | Hua-lei Wang, Yong-bao Ai, Liang Chen, Wen-bin Gu, Wei Li, Fu Lei |
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| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Object detection Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computational Mechanics 02 engineering and technology 01 natural sciences 010305 fluids & plasmas 020901 industrial engineering & automation Vehicle detection Aerial images 0103 physical sciences Computer vision computer.programming_language Swarm UAVs business.industry Mechanical Engineering Metals and Alloys Swarm behaviour Pascal (programming language) Feature pyramid networks Aerial imagery Multi-scale feature fusion Military Science Test set Ceramics and Composites Object detector Artificial intelligence business computer |
| Zdroj: | Defence Technology, Vol 17, Iss 4, Pp 1531-1541 (2021) |
| ISSN: | 2214-9147 |
| DOI: | 10.1016/j.dt.2020.09.018 |
| Popis: | In this paper, based on a bidirectional parallel multi-branch feature pyramid network (BPMFPN), a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swarm unmanned aerial vehicles (UAVs). First, the bidirectional parallel multi-branch convolution modules are used to construct the feature pyramid to enhance the feature expression abilities of different scale feature layers. Next, the feature pyramid is integrated into the single-stage object detection framework to ensure real-time performance. In order to validate the effectiveness of the proposed algorithm, experiments are conducted on four datasets. For the PASCAL VOC dataset, the proposed algorithm achieves the mean average precision (mAP) of 85.4 on the VOC 2007 test set. With regard to the detection in optical remote sensing (DIOR) dataset, the proposed algorithm achieves 73.9 mAP. For vehicle detection in aerial imagery (VEDAI) dataset, the detection accuracy of small land vehicle (slv) targets reaches 97.4 mAP. For unmanned aerial vehicle detection and tracking (UAVDT) dataset, the proposed BPMFPN Det achieves the mAP of 48.75. Compared with the previous state-of-the-art methods, the results obtained by the proposed algorithm are more competitive. The experimental results demonstrate that the proposed algorithm can effectively solve the problem of real-time detection of ground multi-scale targets in aerial images of swarm UAVs. |
| Databáze: | OpenAIRE |
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