Analysis of lane detection techniques using openCV

Vishwakarma, S. K.; Akash, Akash; Yadav, Divakar

doi:10.1109/indicon.2015.7443166

Cited by 15 publications

(5 citation statements)

References 5 publications

(4 reference statements)

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“…Deteksi marka jalan merupakan suatu metode untuk mengetahui lokasi dari marka jalan tanpa diketahui terlebih dahulu noise yang terdapat pada lingkungan sekitarnya. Deteksi ini telah menjadi penelitian yang sering dilakukan oleh banyak orang agar bisa menjadi intelligent transportation system [1] [2]. Namun, penelitian ini masih berlanjut sampai sekarang karena masih terdapat banyak masalah-masalah yang belum bisa diselesaikan.…”

Section: Pendahuluanunclassified

Sistem Deteksi Marka Jalan Berbasis Convolutional Neural Network

Putra

Gustri

2023

jcenim

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Keselamatan dalam berkendara sangatlah penting. Akan tetapi pengendara kurang begitu peduli dengan jalanan, salah satunya marka jalan yang bisa mengakibatkan hal yang tidak diinginkan seperti kecelakaan hingga kemacetan lalu lin- tas. Dengan begitu perlunya pengingat agar pengemudi bisa mengambil tindakan pencegahan, seperti mendeteksi marka yang ada di jalan yang berguna untuk mengontrol dan mempertim- bangkan posisi kendaraan. Deteksi marka jalan yang menggu- nakan Convolutional Neural Network (CNN) sebagai pengindraan kondisi marka jalan sehingga dapat membaca objek badan marka jalan yang mendeteksi dan mengenali bentuk yang ditangkap menggunakan kamera, lalu citra tersebut diterima pada input, diproses, dan output, yang diolah dalam dua dimensi hingga menghasilkan proses yang diharapkan. Library yang digu- nakan pada bidang CNN ini menggunakan TensorFlow, dibantu dengan algoritma You Only Look Once (YOLO). TensorFlow dan YOLO digunakan untuk mengeksekusi perintah dan mengenali objek yang berbeda. Dengan begitu diharapkan kesalahan yang terjadi di lalu lintas semakin kecil.

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Section: Pendahuluanunclassified

Sistem Deteksi Marka Jalan Berbasis Convolutional Neural Network

Putra

Gustri

2023

jcenim

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“…Although the main packages used are Scikit Learn and NodeRED. Scikit Learn is an open-source ML library that supports supervised and unsupervised learning [15]. In this case, a supervised multi-label classification is utilized.…”

Section: Proposed Wsn and ML Modelmentioning

confidence: 99%

An IoT Crop Recommendation System with k-NN and LoRa for Precision Farming

Cruz¹,

Mafra²,

Teixeira³

2022

Anais Do XL Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Choosing the planting site is a complex and decisive task for crop success, but data can help with this task. Wireless Sensor Networks can capture large volumes of data, but manual analysis may be impossible depending on the number of devices and sensors deployed. Furthermore, Machine Learning techniques are handy for processing data and detecting patterns and are widely used nowadays. The union of these two technologies is promising, presenting itself as a path to precision agriculture. This paper proposes a system based on Wireless Sensor Networks capable of detecting the best regions to for cultivating plants such as Kidney Beans, Pomegranate, and Apple. The system uses LoRa technology and Time Division Multiplexing for excellent coverage, various devices at the same channel, and local processing, eliminating the need for the Internet.

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“…The term "self-driving" is often used synonymously with "autonomous" [12]. However, it is something different.…”

Section: Autonomous Self-driving Vehiclesmentioning

confidence: 99%

Autonomous self-driving vehicles - Design of professional laboratory exercises in the field of automotive mechatronics

Schwärz¹,

Schwarz²,

Creutzburg³

2022

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Self-driving cars are gradually making their way into road traffic and represent the most important component of a new form of mobility. Large companies such as Tesla, Google, and Uber are researching the continuous improvement of self-driving vehicles and their reliability. Therefore, it is of great interest for future professionals to become familiar with and master the principles and requirements of autonomous driving. The aim of this work is to develop the innovative concept of a university course at bachelor/master level for students in the automotive industry dealing with autonomous and self-driving cars. Simple hardware is used for the hands-on course, which nevertheless has all the necessary sensors and requirements for a comprehensive practical introduction to the topic of self-driving automotive technology. The course includes lectures and exercises on a variety of topics, including computer vision, the Python programming language, artificial intelligence, the Linux shell, and more.

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Analysis of lane detection techniques using openCV

Cited by 15 publications

References 5 publications

Sistem Deteksi Marka Jalan Berbasis Convolutional Neural Network

Sistem Deteksi Marka Jalan Berbasis Convolutional Neural Network

An IoT Crop Recommendation System with k-NN and LoRa for Precision Farming

Autonomous self-driving vehicles - Design of professional laboratory exercises in the field of automotive mechatronics

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