Like exhaustive search, we aim to capture all possible object locations. Like segmentation, we use the image structure to guide our sampling process. We introduce selective search which combines the strength of both an exhaustive search and segmentation. This paper addresses the problem of generating possible object locations for use in object recognition. In particular, it is able to find the most polluted areas with more accuracy and provides a higher coverage within the time bounds defined by the UAV flight time. The PdUC scheme is compared against various standard mobility models through simulation, showing that it achieves better performance. This way, accurate maps can be constructed in a faster manner when compared to other strategies. Experimental results show that, when using PdUC, an implicit priority guides the construction of pollution maps by focusing on areas where the pollutants’ concentration is higher. Together, they allow automatically performing the monitoring of a specified area using UAVs. These UAVs are guided by our proposed Pollution-driven UAV Control (PdUC) algorithm, which is based on a chemotaxis metaheuristic and a local particle swarm optimization strategy. Embracing this approach, this paper proposes the use of UAVs equipped with off-the-shelf sensors to perform air pollution monitoring tasks. Instead, deploying a fleet of UAVs could be considered an acceptable alternative. Despite the fact that crowdsensing approaches could be an adequate solution for urban areas, they cannot be implemented in rural environments. This work also outperforms the state-of-the-art CNN models, confirming the great potentials of data pre-processing for RF-based UAV state detection.Īir pollution monitoring has recently become an issue of utmost importance in our society. Experiments on a dataset show that, after applying proposed pre-processing methods, the 10-time average accuracy is improved from 46.8% to 91.9%, achieving nearly 50% gain comparing with the benchmark work using the same DNN structure. While existing work mostly focuses on improving the DNN structures, we discover that RF signals' pre-processing before sending them to the classification model is as important as improving the DNN structures. Deep neural networks (DNNs) have been applied for UAV state detection and shown promising potentials. To accurately detect a UAV's working state including hovering and flying, data collection from Radio Frequency (RF) signals is a key step of these strategies and has thus attracted significant research interest. Despite their unprecedented advantages, the increased number of UAVs and their growing threats demand high-performance management and emergency control strategies. Unmanned Aerial Vehicles (UAVs, also called drones) have been widely deployed in our living environments for a range of applications such as healthcare, agriculture, and logistics. Keywords – Drone-assisted Inventory Management, 2D barcode Localization, Image-based Detection To validate the performance of the proposed method, we collect 2D barcode images under real-life warehouse conditions and obtain extensive experiment results. The final detection region is determined by a weighted sum-based score fusion method. To gain discriminant power for classification, SVM is used at the end of the procedure. Visual features of the selected candidate regions are extracted by LBP and HOG methods, respectively. Many regional proposals of 2D barcodes are reduced to a few candidate regions according to the distance information between the drone and the target 2D barcode. In this paper, we propose an efficient detection framework which determines the localizations of 2D barcodes. Additionally, there is an interest in a novel method that automatically detects target barcodes using a IR-based camera, which enables efficient drone path planning and results in reducing power consumption. Drone-assisted inventory management is attractive for companies with large warehouses and factories.
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