2021
DOI: 10.1016/j.dt.2020.10.006
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Multi-scale traffic vehicle detection based on faster R–CNN with NAS optimization and feature enrichment

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Cited by 52 publications
(27 citation statements)
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“…FPN improves model accuracy by extracting multi-scale feature information for fusion. However, due to the reduction of feature channels, a large amount of information will be lost for advanced features, leading to a decrease in the detection accuracy [ 31 ]. To deal with this problem, researchers proposed a receptive field pyramid (RFP) [ 32 ], which can enhance the expressive ability of FPN and enable the network to learn the optimal feature fusion mode.…”
Section: Related Workmentioning
confidence: 99%
“…FPN improves model accuracy by extracting multi-scale feature information for fusion. However, due to the reduction of feature channels, a large amount of information will be lost for advanced features, leading to a decrease in the detection accuracy [ 31 ]. To deal with this problem, researchers proposed a receptive field pyramid (RFP) [ 32 ], which can enhance the expressive ability of FPN and enable the network to learn the optimal feature fusion mode.…”
Section: Related Workmentioning
confidence: 99%
“…Luo et al [ 53 ] presented a model based on the faster RCNN with NAS optimization and feature enrichment to perform the effective detection of multiscale vehicle targets in traffic scenes. Luo et al proposed a Retinex-based image adaptive correction algorithm (RIAC) (to reduce the influence of shadows and illumination), conducted neural architecture search (NAS) on the backbone network used for feature extraction of the faster RCNN (to generate the optimal cross-layer connection to extract multilayer features more effectively), and used the object feature enrichment that combines the multilayer feature information and the context information of the last layer after cross-layer connection (to enrich the information of vehicle targets and improve the robustness of the model for challenging targets such as small scale and severe occlusion).…”
Section: Related Workmentioning
confidence: 99%
“…The detection of vehicles, the most common form of transportation in remote sensing images, can provide data support for many fields. (2) For example, in the field of intelligent transportation system construction, vehicle detection can be applied to road condition information collection and road management information acquisition, and in the field of intelligent traffic management, vehicle detection can be applied to urban traffic flow statistics and road capacity control.…”
Section: Introductionmentioning
confidence: 99%