1. Palm and olive tree detection from UAV images with CNNs: A Comparative Study : YOLOV5 & SSD-Mobilenet.
- Author
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Ayah, Lyacoubi, Kaltoum, Momayiz, and Moha, El-Ayachi
- Subjects
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PALM (Anatomy) , *OLIVE , *NATURAL resources , *DRONE aircraft , *CONVOLUTIONAL neural networks - Abstract
Context and background: Inventories of natural resources especially those of trees allow to make a systematic collection of information of existing trees in a given area. It therefore plays a crucial role in various fields such as forestry, ecological and climate change impact studies. As such, they have become valuable data sources for decision-making regarding the management of natural resources. This is where Drone technology has been able to prove its efficiency in terms of cost and time. Along with DEEP Learning algorithms especially CNNs, they have been able to exceed traditional approaches, in time, cost, and precision. Goal and objectives: This study explores the performance of two widely used algorithms for object detection. The comparison was conducted using two pre-trained CNN models commonly applied in UAV Remote Sensing: Ultralytics' YOLOV5- Large and SSD MobileNET V2 from the TensorFlow model zoo. Methodology: The proposed approach for this study involves five main steps; creating a database, retraining both models on this database, evaluating their performance, and finally comparing their performance. Results: With a learning and accuracy rate of 65% for YOLOV5-Large and 80% for MobileNET SSD V2, both models were able to detect olive and palm trees from UAV images. This work could have been more efficient, if the training dataset was larger and included more palm trees annotations. Overall, YOLOV5-l showed better performance in palm and olive tree detection from UAV images. Its training phase was faster, the detection of objects in the testing phase is also faster, compared to SSD MobileNET V2. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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