An Improved FBPN-Based Detection Network for Vehicles in Aerial Images

Wang, Bin; Gu, Yanfeng

doi:10.3390/s20174709

Cited by 13 publications

(17 citation statements)

References 54 publications

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“…Some algorithms, e.g. [ 21 , 38 ], are evaluated using targets form “small vehicle” only. Some algorithms, e.g.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The APs of the proposed FFDP-CNN detector and other 18 state-of-the-art vehicle detection algorithms for aerial images are shown in Table 7 . It can be seen that the proposed FFDP-CNN detector achieves an AP of 97.34% which is roughly 1.2% higher than the APs achieved by UCAS + NWPU + VS-GANs 2019 [ 42 ] and Improved FBPN-Based Detection Network [ 38 ]. The P-R curve and the detailed performance of the proposed FFDP-CNN detector are shown in Fig 6 and Tables 7 , 8 respectively.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Compared with DFL [ 11 ], the AP has been improved by 3.42%. Compared with Improved FBPN-based Detection Network [ 38 ], the AP has been improved by 2.69%. The P-R curve and the detailed performance of the proposed FFDP-CNN detector are shown in Fig 7 and Table 11 respectively.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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A feature fusion deep-projection convolution neural network for vehicle detection in aerial images

Wang

2021

PLoS ONE

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With the rapid development of Unmanned Aerial Vehicles, vehicle detection in aerial images plays an important role in different applications. Comparing with general object detection problems, vehicle detection in aerial images is still a challenging research topic since it is plagued by various unique factors, e.g. different camera angle, small vehicle size and complex background. In this paper, a Feature Fusion Deep-Projection Convolution Neural Network is proposed to enhance the ability to detect small vehicles in aerial images. The backbone of the proposed framework utilizes a novel residual block named stepwise res-block to explore high-level semantic features as well as conserve low-level detail features at the same time. A specially designed feature fusion module is adopted in the proposed framework to further balance the features obtained from different levels of the backbone. A deep-projection deconvolution module is used to minimize the impact of the information contamination introduced by down-sampling/up-sampling processes. The proposed framework has been evaluated by UCAS-AOD, VEDAI, and DOTA datasets. According to the evaluation results, the proposed framework outperforms other state-of-the-art vehicle detection algorithms for aerial images.

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“…Some algorithms, e.g. [ 21 , 38 ], are evaluated using targets form “small vehicle” only. Some algorithms, e.g.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

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A feature fusion deep-projection convolution neural network for vehicle detection in aerial images

Wang

2021

PLoS ONE

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“…Wang B. et al proposed an Improved FBPN Based Detection Network for small object detection in aerial images. In that paper, an improved feature-balanced pyramid network (FBPN) [37] was designed to balance the high-level and lowlevel feature maps.…”

Section: Related Workmentioning

confidence: 99%

DAR‐Net: Dense Attentional Residual Network for Vehicle Detection in Aerial Images

Wang

2021

Computational Intelligence and Neuroscience

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With the rapid development of deep learning and the wide usage of Unmanned Aerial Vehicles (UAVs), CNN-based algorithms of vehicle detection in aerial images have been widely studied in the past several years. As a downstream task of the general object detection, there are some differences between the vehicle detection in aerial images and the general object detection in ground view images, e.g., larger image areas, smaller target sizes, and more complex background. In this paper, to improve the performance of this task, a Dense Attentional Residual Network (DAR-Net) is proposed. The proposed network employs a novel dense waterfall residual block (DW res-block) to effectively preserve the spatial information and extract high-level semantic information at the same time. A multiscale receptive field attention (MRFA) module is also designed to select the informative feature from the feature maps and enhance the ability of multiscale perception. Based on the DW res-block and MRFA module, to protect the spatial information, the proposed framework adopts a new backbone that only downsamples the feature map 3 times; i.e., the total downsampling ratio of the proposed backbone is 8. These designs could alleviate the degradation problem, improve the information flow, and strengthen the feature reuse. In addition, deep-projection units are used to reduce the impact of information loss caused by downsampling operations, and the identity mapping is applied to each stage of the proposed backbone to further improve the information flow. The proposed DAR-Net is evaluated on VEDAI, UCAS-AOD, and DOTA datasets. The experimental results demonstrate that the proposed framework outperforms other state-of-the-art algorithms.

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“…In order to strengthen the detection of small targets, YOLOv3 algorithm draws on Feature Pyramid Network (FPN). e high-level feature and the shallow feature information are fused, and the multiple-scale fusion method is used to perform position and category prediction on multiple-scale feature maps [28]. However, the three-scale feature fusion method adopted by the YOLOv3 network structure has an adverse effect on the detection of smaller targets in the surveillance video.…”

Section: Taxi Detection Based On Improved Yolov3mentioning

confidence: 99%

Recognition of Taxi Violations Based on Semantic Segmentation of PSPNet and Improved YOLOv3

Yang

2021

Scientific Programming

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Taxi has the characteristics of strong mobility and wide dispersion, which makes it difficult for relevant law enforcement officers to make accurate judgment on their illegal acts quickly and accurately. With the investment of intelligent transportation system, image analysis technology has become a new method to determine the illegal behavior of taxis, but the current image analysis method is still difficult to support the detection of illegal behavior of taxis in the actual complex image scene. To solve this problem, this study proposed a method of taxi violation recognition based on semantic segmentation of PSPNet and improved YOLOv3. (1) Based on YOLOv3, the proposed method introduces spatial pyramid pooling (SPP) for taxi recognition, which can convert vehicle feature images with different resolutions into feature vectors with the same dimension as the full connection layer and solve the problem of repeated extraction of YOLOv3 vehicle image features. (2) This method can recognize two different violations of taxi (blocking license plate and illegal parking) rather than only one. (3) Based on PSPNet semantic segmentation network, a taxi illegal parking detection method is proposed. This method can collect the global information of road condition images and aggregate the image information of different regions, so as to improve the ability to obtain the global information orderly and improve the accuracy of taxi illegal parking detection. The experimental results show that the proposed method has excellent recognition performance for the detection rate of license plate occlusion behavior DR is 85.3%, and the detection rate of taxi illegal parking phenomenon DR is 96.1%.

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An Improved FBPN-Based Detection Network for Vehicles in Aerial Images

Cited by 13 publications

References 54 publications

A feature fusion deep-projection convolution neural network for vehicle detection in aerial images

A feature fusion deep-projection convolution neural network for vehicle detection in aerial images

DAR‐Net: Dense Attentional Residual Network for Vehicle Detection in Aerial Images

Recognition of Taxi Violations Based on Semantic Segmentation of PSPNet and Improved YOLOv3

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