Joint-SRVDNet: Joint Super Resolution and Vehicle Detection Network

In many domestic and military applications, aerial vehicle detection and super-resolutionalgorithms are frequently developed and applied independently. However, aerial vehicle detection on super-resolved images remains a challenging task due to the lack of discriminative information in the super-resolved images. To address this problem, we propose a Joint Super-Resolution and Vehicle DetectionNetwork (Joint-SRVDNet) that tries to generate discriminative, high-resolution images of vehicles fromlow-resolution aerial images. First, aerial images are up-scaled by a factor of 4x using a Multi-scaleGenerative Adversarial Network (MsGAN), which has multiple intermediate outputs with increasingresolutions. Second, a detector is trained on super-resolved images that are upscaled by factor 4x usingMsGAN architecture and finally, the detection loss is minimized jointly with the super-resolution loss toencourage the target detector to be sensitive to the subsequent super-resolution training. The network jointlylearns hierarchical and discriminative features of targets and produces optimal super-resolution results. Weperform both quantitative and qualitative evaluation of our proposed network on VEDAI, xView and DOTAdatasets. The experimental results show that our proposed framework achieves better visual quality than thestate-of-the-art methods for aerial super-resolution with 4x up-scaling factor and improves the accuracy ofaerial vehicle detection

AERIAL SURVEILLANCE FOR VEHICLE DETECTION USING DBN AND CANNY EDGE DETECTOR

We present an automatic vehicle detection system for aerial surveillance in this paper. In this system, we escape from the stereotype and existing frameworks of vehicle detection in aerial surveillance, which are either region based or sliding window based. We design a pixel wise classification method for vehicle detection. The novelty lies in the fact that, in spite of performing pixel wise classification, relations among neighboring pixels in a region are preserved in the feature extraction process. We consider features including vehicle colors and local features. For vehicle color extraction, we utilize a color transform to separate vehicle colors and non-vehicle colors effectively. For edge detection, we apply moment preserving to adjust the thresholds of the Canny edge detector automatically, which increases the adaptability and the accuracy for detection in various aerial images. Afterward, a dynamic Bayesian network (DBN) is constructed for the classification purpose. We convert regional local features into quantitative observations that can be referenced when applying pixel wise classification via DBN. Experiments were conducted on a wide variety of aerial videos. The results demonstrate flexibility and good generalization abilities of the proposed method on a challenging data set with aerial surveillance images taken at different heights and under different camera angles

Vehicle Detection in Aerial Images

Bakalářská práce se zabývá implementací aplikace pro detekci vozidel v leteckých snímcích. Tato aplikace může být uplatněna v dopravní bezpečnosti pro detekci kolon a nehod, nebo s asistenčními prvky jako jsou informace o obsazenosti parkovišť a hustoty provozu. Pro výslednou aplikaci byla zvolena architektura YOLO, která je založena na technologii neuronových sítí a jejich schopnostech učit se poznávat vozidla pomocí trénování sítě z vytvořeného datasetu. Bakalářská práce kromě aplikace rozebírá tématiku potřebnou k pochopení zvoleného řešení a výsledné porovnání různých architektur na zadaném problému.This bachelor's thesis is about the implementation of an application for vehicle detection in aerial images. This application can be used in traffic safety for traffic jams and accident detection or in assistance elements such as information about the availability of spots in parking lots or density of traffic. The final application is based on YOLO architecture which is based on a neural network and its ability to learn from training with a custom dataset. Bachelor's thesis besides application explains theory which is needed to understand the topic and finally a comparison of different architectures on the given problem.460 - Katedra informatikyvýborn

Multi-object Tracking in Aerial Image Sequences using Aerial Tracking Learning and Detection Algorithm

Vison based tracking in aerial images has its own significance in the areas of both civil and defense applications.  A novel algorithm called aerial tracking learning detection which works on the basis of the popular tracking learning detection algorithm to effectively track single and multiple objects in aerial images is proposed in this study. Tracking learning detection (TLD) considers both appearance and motion features for tracking. It can handle occlusion to certain extent, and can work well on long duration video sequences. However, when objects are tracked in aerial images taken from platforms like unmanned air vehicle, the problems of frequent pose change, scale and illumination variations arise adding to low resolution, noise and jitter introduced by motion of the camera.  The proposed algorithm incorporates compensation for the camera movement, algorithmic modifications in combining appearance and motion cues for detection and tracking of multiple objects and enhancements in the form of inter object distance measure for improved performance of the tracker when there are many identical objects in proximity. This algorithm has been tested on a large number of aerial sequences including benchmark videos, TLD dataset and many classified unmanned air vehicle sequences and has shown better performance in comparison to TLD.

A Study on the Implementation of YOLOv4 Algorithm with Hyperparameter Tuning for Car Detection in Unmanned Aerial Vehicle Images

Unmanned Aerial Vehicles (UAVs) for surveillance and monitoring have become more prevalent due to their versatility and mobility. These vehicles capture highresolution images that provide a broad field of view in real-time. Today, enhancing object detection accuracy on images captured by unmanned aerial vehicles (UAVs) has become a significant challenge. Through extensive research, it has been established that the correct setup of hyperparameters is imperative to achieving the highest accuracy in machine learning. Our study introduces a technique that utilizes hyperparameter tuning to implement the YOLOv4 algorithm, enabling the detection of cars in unmanned aerial vehicle images. In general, all scenarios of this study have different accuracy results, which have implications for their detectability. Thus, scenario 3 of YOLOv4 hyperparameter tuning is the best model accuracy. Our approach utilizes the PSU Aerial Car Images Dataset from previous studies. During this research, accuracy values were obtained through testing at the model validation stage rather than at the testing stage. In this study, we achieved a validation performance of the detection model by using a validation dataset proportion of 20%. Based on our research, it has been revealed that the YOLOv4 algorithm is a highly efficient car detection system when it comes to unmanned aerial vehicle images. Through rigorous testing of multiple hyperparameter tuning scenarios, we achieved an exceptional accuracy of 99.02% in the optimal model scenario, which utilized YOLOv4. Similarly, in replicating a research paper's hyperparameter tuning methods on YOLOv3, the highest accuracy of 98.40% was attained in scenario 2

Deep Learning based Vehicle Detection in Aerial Imagery

Detecting vehicles in aerial images is an important task for many applications like traffic monitoring or search and rescue work. In recent years, several deep learning based frameworks have been proposed for object detection. However, these detection frameworks were developed and optimized for datasets that exhibit considerably differing characteristics compared to aerial images, e.g. size of objects to detect. In this report, we demonstrate the potential of Faster R-CNN, which is one of the state-of-theart detection frameworks, for vehicle detection in aerial images. Therefore, we systematically investigate the impact of adapting relevant parameters. Due to the small size of vehicles in aerial images, the most improvement in performance is achieved by using features of shallower layers to localize vehicles. However, these features offer less semantic and contextual information compared to features of deeper layers. This results in more false alarms due to objects with similar shapes as vehicles. To account for that, we further propose a deconvolutional module that up-samples features of deeper layers and combines these features with features of shallower layers

Highway traffic monitoring on medium resolution satellite images

International audienceThese last years, earth observation imagery has significantly improved. Public satellites such as WorldView-3 can now produce images with a Ground Sample Distance of 31cm, reaching an equivalent resolution than aerial images. Perhaps more importantly, the revisit frequency has also been greatly enhanced: providers such as Planet can now acquire images of an area on a daily basis. These major improvements are fueled by an increasing demand for frequent objects detection. An application generating a particular interest is vehicle detection. Indeed, vehicle detection can give to public and private actors valuable data such as traffic monitoring and parking occupancy rate estimations. Several datasets, such as DOTA or VehSat, already exist, allowing researchers to train machine learning algorithms to detect vehicles. However, these datasets focus on relatively high definition and expensive aerial and satellite images. In this paper, we will present a method for detecting vehicles on medium resolution satellite images, with a GSD comprised between 1 and 5 meters. This approach can notably be used on Planet images, allowing to monitor traffic of an area on a daily basis

Complete Model for Automatic Object Detection and Localisation on Aerial Images using Convolutional Neural Networks

In this paper, a novel approach for an automatic object detection and localisation on aerial images is proposed. Proposed model does not use ground control points (GCPs) and consists of three major phases. In the first phase, optimal flight route is planned in order to capture the area of interest and aerial images are acquired using unmanned aerial vehicle (UAV), followed by creating a mosaic of collected images to obtained larger field-of-view panoramic image of the area of interest and using the obtained image mosaic to create georeferenced map. The image mosaic is then also used to detect objects of interest using the approach based on convolutional neural networks