review · Applied Sciences
Target detection is critical for managing traffic congestion in smart cities as well as for military, civilian, and sports purposes. Unmanned aerial vehicles offer an appealing tool for monitoring traffic because of their mobility, low cost, broad field of view, safety, and straightforward operation. However, detecting targets from aerial imagery remains difficult due to background movement, severe occlusion, unclear object traits, and the small scale of objects, which often leads to lost detail and poor model performance. Deep learning techniques address these limitations through one-stage and two-stage detectors designed to identify targets under challenging conditions. Reviewing these end-to-end detection paradigms helps clarify how to improve recognition accuracy, lower computational expenses, and optimise system designs across varied operational settings.
Traffic congestion creates significant challenges for modern urban centres, and conventional monitoring methods often struggle with occluded or fast-moving vehicles. Understanding how drone-based imagery combined with deep learning can accurately detect small, obscured objects enables planners and transportation authorities to explore more flexible, cost-effective aerial surveillance tools for keeping city roads moving efficiently.
This review highlights applications for municipal traffic managers and smart city monitoring teams seeking to alleviate roadway congestion through drone surveillance. Because the work evaluates and compares existing deep learning detectors rather than validating a newly deployed commercial tool, it reflects early-stage research. Practical commercialisation would require developers to embed these optimised models into operational flight hardware and city management systems.
AI-generated from the published abstract. Always read the original work before citing.
In smart cities, target detection is one of the major issues in order to avoid traffic congestion. It is also one of the key topics for military, traffic, civilian, sports, and numerous other applications. In daily life, target detection is one of the challenging and serious tasks in traffic congestion due to various factors such as background motion, small recipient size, unclear object characteristics, and drastic occlusion. For target examination, unmanned aerial vehicles (UAVs) are becoming an engaging solution due to their mobility, low cost, wide field of view, accessibility of trained manipulators, a low threat to people’s lives, and ease to use. Because of these benefits along with good tracking effectiveness and resolution, UAVs have received much attention in transportation technology for tracking and analyzing targets. However, objects in UAV images are usually small, so after a neural estimation, a large quantity of detailed knowledge about the objects may be missed, which results in a deficient performance of actual recognition models. To tackle these issues, many deep learning (DL)-based approaches have been proposed. In this review paper, we study an end-to-end target detection paradigm based on different DL approaches, which includes one-stage and two-stage detectors from UAV images to observe the target in traffic congestion under complex circumstances. Moreover, we also analyze the evaluation work to enhance the accuracy, reduce the computational cost, and optimize the design. Furthermore, we also provided the comparison and differences of various technologies for target detection followed by future research trends.
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DOI: 10.3390/app13063995
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