Abstract
Abstract. Despite its high accuracy and fast speed in object detection, Single Shot Multi-Box Detector (SSD) tends to get undesirable results especially for small targets such as vehicles on high-resolution images. In this paper, we propose a new convolutional neural network based on SSD to detect vehicles on high-resolution images. In the proposed framework, the feature fusion module and detection module are incorporated. In the feature fusion module, feature maps of different scales are integrated into a fusion feature for object detection, which could improve the accuracy effectively. Besides, to prevent the network from overfitting and speed up the training, the batch normalization layer is embedded between the detection layers in the detection module. Some ablation experiments provide strong evidence for the effectiveness of these above structures. On the UCAS-High Resolution Aerial Object Detection Dataset, our network has the ability to achieve the 0.904 AP (average precision) with 0.094 AP higher than SSD512 but similar speed to it.
Highlights
With the development of society and economy, the number of vehicles is constantly increasing
We propose a convolutional neural network based on Single Shot Multi-Box Detector (SSD) for vehicle detection
We proposed a new convolutional neural network based on SSD for vehicle detection, which applies feature fusion and batch normalization layers together on it
Summary
With the development of society and economy, the number of vehicles is constantly increasing. Monitoring traffic conditions enables the related transportation department to better control traffic and plan road. Accurate monitoring can avoid traffic accidents and ease traffic jams. Remote sensing technology featuring rich information, low cost and wide coverage, which is widely used in traffic applications.(Sakai et al, 2019). As one of the important applications, vehicle detection can be applied in traffic monitoring, road planning, target tracking, etc.(Tang et al, 2017b). Vehicle detection in remote sensing images has attracted more and more attention in recent years. The existing approaches for vehicle detection in remote sensing images could be categorized into three types, including computer vision methods, traditional machine learning methods and deep learning
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