Multi-Scale and Occlusion Aware Network for Vehicle Detection and Segmentation on UAV Aerial Images

With the advantage of high maneuverability, Unmanned Aerial Vehicles (UAVs) have been widely deployed in vehicle monitoring and controlling. However, processing the images captured by UAV for the extracting vehicle information is hindered by some challenges including arbitrary orientations, huge scale variations and partial occlusion. In seeking to address these challenges, we propose a novel Multi-Scale and Occlusion Aware Network (MSOA-Net) for UAV based vehicle segmentation, which consists of two parts including a Multi-Scale Feature Adaptive Fusion Network (MSFAF-Net) and a Regional Attention based Triple Head Network (RATH-Net). In MSFAF-Net, a self-adaptive feature fusion module is proposed, which can adaptively aggregate hierarchical feature maps from multiple levels to help Feature Pyramid Network (FPN) deal with the scale change of vehicles. The RATH-Net with a self-attention mechanism is proposed to guide the location-sensitive sub-networks to enhance the vehicle of interest and suppress background noise caused by occlusions. In this study, we release a large comprehensive UAV based vehicle segmentation dataset (UVSD), which is the first public dataset for UAV based vehicle detection and segmentation. Experiments are conducted on the challenging UVSD dataset. Experimental results show that the proposed method is efficient in detecting and segmenting vehicles, and outperforms the compared state-of-the-art works.

SEGMENT-AND-COUNT: VEHICLE COUNTING IN AERIAL IMAGERY USING ATROUS CONVOLUTIONAL NEURAL NETWORKS

<p><strong>Abstract.</strong> High-resolution aerial imagery can provide detailed and in some cases even real-time information about traffic related objects. Vehicle localization and counting using aerial imagery play an important role in a broad range of applications. Recently, convolutional neural networks (CNNs) with atrous convolution layers have shown better performance for semantic segmentation compared to conventional convolutional aproaches. In this work, we propose a joint vehicle segmentation and counting method based on atrous convolutional layers. This method uses a multi-task loss function to simultaneously reduce pixel-wise segmentation and vehicle counting errors. In addition, the rectangular shapes of vehicle segmentations are refined using morphological operations. In order to evaluate the proposed methodology, we apply it to the public “DLR 3K” benchmark dataset which contains aerial images with a ground sampling distance of 13<span class="thinspace"></span>cm. Results show that our proposed method reaches 81.58<span class="thinspace"></span>% mean intersection over union in vehicle segmentation and shows an accuracy of 91.12<span class="thinspace"></span>% in vehicle counting, outperforming the baselines.</p>

Segmentation Plate and Number Vehicle using Integral Projection

Ticket system on the shopping mall and offices have a way to write it by the manual and automatic way. Most of the systems in use are by manual rather than automatic. With the problem, the manual system will be replaced with an automatic system that can recognize the number on the license plate. One method used to detect the plate and number that can be used is the method of finding contour. Finding contour can locate the number plate by detecting the rectangular shape of an image. This method is very important to be able to detect the number plate because there are many forms contained in the vehicle. Segmentation is used to recognize the characters on the car number plate using integral projection. Integral projection can separate the characters contained on the car number plate to facilitate processing on character recognition. The successful use of this method is 98%. errors are usually caused by the faded car plate colors

Segmentation Plate and Number Vehicle using Integral Projection

Ticket system on the shopping mall and offices have a way to write it by the manual and automatic way. Most of the systems in use are by manual rather than automatic. With the problem, the manual system will be replaced with an automatic system that can recognize the number on the license plate. One method used to detect the plate and number that can be used is the method of finding contour. Finding contour can locate the number plate by detecting the rectangular shape of an image. This method is very important to be able to detect the number plate because there are many forms contained in the vehicle. Segmentation is used to recognize the characters on the car number plate using integral projection. Integral projection can separate the characters contained on the car number plate to facilitate processing on character recognition. The successful use of this method is 98%. errors are usually caused by the faded car plate colors