Details

Autor(en) / Beteiligte

• Brassai, Sandor Tihamer
• NEMeth, Andras
• Hammas, Attila
• GABor, Szabolcs Laszlo

Titel

Deep learning based object detection for agricultural machinery

Ist Teil von

• 2022 IEEE 22nd International Symposium on Computational Intelligence and Informatics and 8th IEEE International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics (CINTI-MACRo), 2022, p.000089-000094

Ort / Verlag

IEEE

Erscheinungsjahr

2022

Quelle

IEEE/IET Electronic Library (IEL)

Beschreibungen/Notizen

• Drone imagery based object supervising has become more and more widespread. In the paper the Single shot Alignment Network is used to classify and localize the objects. The images were acquired by using two types of drones, DJI Tello and Zll SG906 Pro 2 in about thirty classes, and about nineteen were processed and detailed in the paper. The objects labeling was realized in CVAT labeling tool. For neural network management the mmdetection framework was used, and the obtained results were detailed on s2a-net. The paper focuses on the preparation of the neural network system to be used for agricultural machine detection. The network was trained on a PC with reduced processing capabilities. The dataset was cut in smaller tasks. An architecture is proposed to be used in future for dataset management during the training process.