Dalam era yang terus berubah, intelligent transportation systems (ITS) menjadi pusat perhatian dalam penelitian yang inovatif. Temukan bagaimana ITS membentuk kembali pemantauan lalu lintas dengan solusi inovatif, terutama dalam klasifikasi kendaraan darurat yang kritis. Membuka potensi teknologi computer vision, termasuk convolutional neural networks (CNN), untuk merevolusi keselamatan lalu lintas. Jelajahi bagaimana CNN mengubah klasifikasi kendaraan darurat, memberikan peringatan dini, dan memungkinkan respons cepat selama krisis.
Penelitian terbaru oleh Kherraki dan Ouazzani (2022) menyoroti klasifikasi kendaraan darurat menggunakan output dari kamera closed circuit television (CCTV). Temuan mereka memberikan wawasan mendalam tentang topik yang vital ini, menekankan urgensi dalam memprioritaskan kendaraan darurat untuk menyelamatkan nyawa. Mereka juga mengungkap arsitektur CNN yang paling efektif untuk klasifikasi kendaraan darurat secara real-time, dengan membandingkan delapan model berbeda menggunakan Analytics Vidhya Emergency Vehicle dataset. Hasil penelitian mereka menegaskan bahwa DenseNet121 menjadi pilihan optimal dengan hasil klasifikasi yang luar biasa, serta kemampuan untuk mengurangi kebutuhan memori untuk aplikasi real-time.
Bergabunglah bersama kami dalam mendorong keselamatan lalu lintas dan menyelamatkan nyawa melalui teknologi canggih dalam klasifikasi kendaraan darurat.
Nowadays, intelligent transportation system (ITS) has become one of the most popular subjects of scientific research. ITS provides innovative services to traffic monitoring. The classification of emergency vehicles in traffic surveillance cameras provides an early warning to ensure a rapid reaction in emergency events. Computer vision technology, including deep learning, has many advantages for traffic monitoring. For instance, convolutional neural network (CNN) has given very good results and optimal performance in computer vision tasks, such as the classification of vehicles according to their types, and brands. In this paper, we will classify emergency vehicles from the output of a closed-circuit television (CCTV) camera. Among the advantages of this research paper is providing detailed information on the emergency vehicle classification topic. Emergency vehicles have the highest priority on the road and finding the best emergency vehicle classification model in realtime will undoubtedly save lives. Thus, we have used eight CNN architectures and compared their performances on the Analytics Vidhya Emergency Vehicle dataset. The experiments show that the utilization of DenseNet121 gives excellent classification results which makes it the most suitable architecture for this research topic, besides, DenseNet121 does not require a high memory size which makes it appropriate for real-time applications.
Deep convolutional neural networks architecture for an efficient emergency vehicle classification in real-time traffic monitoring
Amine Kherraki, Rajae El Ouazzani
Redaksi: I. Busthomi