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A Less Complexity Deep Learning Method for Drones Detection
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class="card-body"><strong>Paper Count:</strong> 87352</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">A Less Complexity Deep Learning Method for Drones Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamad%20Kassab">Mohamad Kassab</a>, <a href="https://publications.waset.org/abstracts/search?q=Amal%20El%20Fallah%20Seghrouchni"> Amal El Fallah Seghrouchni</a>, <a href="https://publications.waset.org/abstracts/search?q=Frederic%20Barbaresco"> Frederic Barbaresco</a>, <a href="https://publications.waset.org/abstracts/search?q=Raed%20Abu%20Zitar"> Raed Abu Zitar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Detecting objects such as drones is a challenging task as their relative size and maneuvering capabilities deceive machine learning models and cause them to misclassify drones as birds or other objects. In this work, we investigate applying several deep learning techniques to benchmark real data sets of flying drones. A deep learning paradigm is proposed for the purpose of mitigating the complexity of those systems. The proposed paradigm consists of a hybrid between the AdderNet deep learning paradigm and the Single Shot Detector (SSD) paradigm. The goal was to minimize multiplication operations numbers in the filtering layers within the proposed system and, hence, reduce complexity. Some standard machine learning technique, such as SVM, is also tested and compared to other deep learning systems. The data sets used for training and testing were either complete or filtered in order to remove the images with mall objects. The types of data were RGB or IR data. Comparisons were made between all these types, and conclusions were presented. <iframe src="https://publications.waset.org/abstracts/154403.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drones%20detection" title="drones detection">drones detection</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=birds%20versus%20drones" title=" birds versus drones"> birds versus drones</a>, <a href="https://publications.waset.org/abstracts/search?q=precision%20of%20detection" title=" precision of detection"> precision of detection</a>, <a href="https://publications.waset.org/abstracts/search?q=AdderNet" title=" AdderNet"> AdderNet</a> </p> <a href="https://publications.waset.org/abstracts/154403/a-less-complexity-deep-learning-method-for-drones-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154403.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">182</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul 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