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.