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Likewise, object detection in overhead images provides insight for use in many real-world applications yet is still challenging because of substantial image volumes, inconsistent image resolution, small-sized objects, highly complex backgrounds, and nonuniform object classes. In particular, overhead images combined with computer vision allow many real-world uses for economic, commercial, and humanitarian purposes, including assessing economic impact from access crop yields, financial supply chain prediction for company’s revenue management, and rapid disaster surveillance system (wildfire alarms, rising sea levels, weather forecast). Significant advancements and progress made in recent computer vision research enable more effective processing of various objects in high-resolution overhead imagery obtained by various sources from drones, airplanes, and satellites. Various research challenges and future directions for object detection also has been discussed in this research paper. With the remarkable success of deep neural networks in object detection, the performance of the detectors has improved. In addition, a comparative analysis has been performed and analyzed that the proposed techniques are different in their architecture, optimization function, and training strategies. Our contribution to the current research work is (i) analysis of traditional, two-stage, one-stage object detection techniques, (ii) Dataset preparation and available standard dataset, (iii) Annotation tools, and (iv) performance evaluation metrics. A systematic review has been followed to summarize the current research work’s findings and discuss seven research questions related to object detection. This study provides a detailed literature review focusing on object detection and discusses the object detection techniques. Over the past, it has gained much attention to do more research on computer vision tasks such as object classification, counting of objects, and object monitoring.

Object detection is one of the most fundamental and challenging tasks to locate objects in images and videos. Keywords-Super Resolution, Deep Learning, Generative Adversarial Networks, Aerial Images. better reconstruction quality, is the one trained using the transfer learning technique. better perceptual quality, on xView and DOTA validation datasets is the one trained using the DIV2K dataset and the model which gives the best RMSEresults, i.e. We have seen that the model which gives the best PI results, i.e. Perceptual Index (PI) and Root Mean Squared Error (RMSE) which are used on European Conference on Computer VisionPerceptual Image Restoration and Manipulation Workshop 2018 are computed as the performance metrics. Furthermore, DIV2K dataset is used to pre-train a generative model, and then the transfer learning technique is used to train separate models on xView and DOTA validation datasets. Public xView and DOTA datasets which contain images mostly captured by satellites around the world are used to train a generative model via SRGAN architecture. We also applied transfer learning methodology to achieve better image quality. To achieve this Super Resolution Generative Adversarial Network (SRGAN), which is based on the Generative Adversarial Networks architecture is used. In this work, we aim to apply the Super Resolution method to the Aerial Images captured for surveillance to enable more information about the original scenes. A super-resolved image, which is created using an original image captured by an imaging source is not only more meaningful to human perception but also has advantages on downstream tasks such as object detection and pattern recognition. Images created using the Super Resolution method can generate more information compared to their low resolution counterparts. Thus, our final proposal for aerial surveillance systems contains the pre-trained YOLOv5圆 architecture with attention on performance maximization for multiple specific aerial scenarios, processing 50km2 in no more than 6 seconds.

Experiments have been conducted over a dataset from Google Earth platform.

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Then, applying different training strategies and using a series of good practices, we achieved a F1-score of 0.820 and a considerable of 0.809 for the more difficult detection class. First, we tackle the scarcity of multi-class aircraft detection data by developing of a 50 cm GSD dataset.

Motivated by the state-of-art object detection methods for aerial sensing images, we explored and achieved satisfactory results based on standard YOLO archi-tectures by providing an analysis of certain aerial scenarios, from chained to scattered or yin-yang pairs objects and model's performance comparison on a typical wild emergency situation. Multi-class aircraft recognition is important in aerial surveillance applications to make consistent proposals for decision makers.

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