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

Abstract

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.

Exploiting Remote Sensing Imagery for Vehicle Detection and Classification Using an Artificial Intelligence Technique

“…Considering that BiFPN exploits feature levels 3-7, the input resolution should be dividable by 2 7 = 128, which implies that it linearly improves the resolution by using the following equation:…”

“…Note that (z z) = 0 for z = z. KL divergence is added to the MSE for the minimization of cost. Thus, the cost function C(x, y; θ) is formulated in (7):…”

“…Vehicle detection in RSI aims to identify each instance of a vehicle [6]. In previous approaches, researchers often developed and extracted vehicle features manually and then classified them to attain vehicle detection [7]. The fundamental objective is to extract vehicle features and utilize traditional machine learning (ML) techniques for classification.…”

Application of Dual‐Channel Convolutional Neural Network Algorithm in Semantic Feature Analysis of English Text Big Data

“…Wang and Xu proposed a feature fusion depth projection CNN, which mainly used a new residual block, stepwise res block, to mine high-level semantic features while retaining low-level details. The framework used a specially designed feature fusion module to further balance the features obtained from different levels of the backbone network [ 6 ]. Javed et al proposed a new generation antagonism network.…”