{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/takunet-an-energy-efficient-cnn-for-real-time","title":"TakuNet: an Energy-Efficient CNN for Real-Time Inference on Embedded UAV systems in Emergency Response Scenarios","arxiv_id":"2501.05880","date":"2025-01-10","proceeding":null,"authors":["Daniel Rossi","Guido Borghi","Roberto Vezzani"],"abstract":"Designing efficient neural networks for embedded devices is a critical challenge, particularly in applications requiring real-time performance, such as aerial imaging with drones and UAVs for emergency responses. In this work, we introduce TakuNet, a novel light-weight architecture which employs techniques such as depth-wise convolutions and an early downsampling stem to reduce computational complexity while maintaining high accuracy. It leverages dense connections for fast convergence during training and uses 16-bit floating-point precision for optimization on embedded hardware accelerators. Experimental evaluation on two public datasets shows that TakuNet achieves near-state-of-the-art accuracy in classifying aerial images of emergency situations, despite its minimal parameter count. Real-world tests on embedded devices, namely Jetson Orin Nano and Raspberry Pi, confirm TakuNet's efficiency, achieving more than 650 fps on the 15W Jetson board, making it suitable for real-time AI processing on resource-constrained platforms and advancing the applicability of drones in emergency scenarios. The code and implementation details are publicly released.","url_abs":"https://arxiv.org/abs/2501.05880v3","url_pdf":"https://arxiv.org/pdf/2501.05880v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"takunet-an-energy-efficient-cnn-for-real-time","repo_url":"https://github.com/danielrossi1/takunet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"aerial-scene-classification","task_name":"Aerial Scene Classification"},{"task_slug":null,"task_name":"CPU"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":null,"task_name":"NVIDIA Jetson Orin Nano"},{"task_slug":"raspberry-pi-3","task_name":"Raspberry Pi 3"},{"task_slug":"raspberry-pi-4","task_name":"Raspberry Pi 4"},{"task_slug":"raspberry-pi-5","task_name":"Raspberry Pi 5"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"depthwise-convolution","method_name":"Depthwise Convolution"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"},{"method_slug":"pointwise-convolution","method_name":"Pointwise Convolution"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-aider","task":"Image Classification","dataset":"AIDER","model":"TakuNet FP=16","rank_in_archive_order":1,"of":1,"metrics":{"Test F1 score":"0.943"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-aiderv2","task":"Image Classification","dataset":"AIDERV2","model":"TakuNet FP=16","rank_in_archive_order":1,"of":1,"metrics":{"Test F1 score":"0.958"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}