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Dense construction vehicle detection based on orientation-aware feature fusion convolutional neural network
Abstract During the construction process, many construction vehicles gather in a small area in a short period, thus the accurate identification of dense multiple vehicles is of great significance for ensuring the safety of construction sites. In this study, a novel end-to-end deep learning network, namely orientation-aware feature fusion single-stage detection (OAFF-SSD), is proposed for the precise detection of dense multiple construction vehicles using images from Unmanned Aerial Vehicle (UAV). The proposed OAFF-SSD consists of three main modules: (1) multi-level feature extraction, (2) novel feature fusion, and (3) new orientation-aware bounding box (OABB) proposal and regression. Meanwhile, specific strategies are designated for the fast convergence of training losses. The application of OAFF-SSD to real construction sites vehicle detection and comparison with the well-known SSD (a benchmark using traditional bounding box) and orientation-aware SSD (OA-SSD) demonstrate the efficiency and accuracy of the proposed method.
Highlights An end-to-end deep learning approach is proposed to detect vehicles from UAV. An OABB proposal module is presented for dense multiple detection. A feature fusion module is added to the network for more precise detection.
Dense construction vehicle detection based on orientation-aware feature fusion convolutional neural network
Abstract During the construction process, many construction vehicles gather in a small area in a short period, thus the accurate identification of dense multiple vehicles is of great significance for ensuring the safety of construction sites. In this study, a novel end-to-end deep learning network, namely orientation-aware feature fusion single-stage detection (OAFF-SSD), is proposed for the precise detection of dense multiple construction vehicles using images from Unmanned Aerial Vehicle (UAV). The proposed OAFF-SSD consists of three main modules: (1) multi-level feature extraction, (2) novel feature fusion, and (3) new orientation-aware bounding box (OABB) proposal and regression. Meanwhile, specific strategies are designated for the fast convergence of training losses. The application of OAFF-SSD to real construction sites vehicle detection and comparison with the well-known SSD (a benchmark using traditional bounding box) and orientation-aware SSD (OA-SSD) demonstrate the efficiency and accuracy of the proposed method.
Highlights An end-to-end deep learning approach is proposed to detect vehicles from UAV. An OABB proposal module is presented for dense multiple detection. A feature fusion module is added to the network for more precise detection.
Dense construction vehicle detection based on orientation-aware feature fusion convolutional neural network
Guo, Yapeng (author) / Xu, Yang (author) / Li, Shunlong (author)
2020-02-04
Article (Journal)
Electronic Resource
English
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