RETRACTED ARTICLE: Efficient image-based object detection for floating weed collection with low cost unmanned floating vehicles

• Published in: Soft computing (Berlin, Germany) Germany), 2021-10, Vol.25 (20), p.13093-13101
• Main Authors: Sravanthi, Ravi, Sarma, A. S. V.
• Format: Article
• Language: English
• Subjects: Application of Soft Computing; Artificial Intelligence; Computational Intelligence; Control; Engineering; Mathematical Logic and Foundations; Mechatronics; Robotics
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-021-06171-9

Unmanned floating vehicles (UFVs) can play a major role in the removal of invasive floating aquatic weeds in the water bodies. To provide automation to those UFVs, image or video processing techniques are helpful. Waterline detection from the sky–land–water region is the primary step for UFV autonomous navigation. After detecting the water region, object detection in the water region is the secondary step. In this paper, an object detection algorithm in water for low cost UFVs is proposed. Real-time and accurate waterline detection is done by using the K-means algorithm and Fast Marching method and it is compared with the Hough transform. Objects are detected accurately in the water region by a modified gradient-based image processing algorithm. In this algorithm, instead of Sobel Gradient Operator, Finite Difference Central Gradient Operator is used to achieve high accuracy in detecting objects in the water region. Floating green color weeds are also detected from objects using the green color of the weeds, and weed percentage is also calculated. Experiments were done on Own datasets, and OSF datasets containing freely floating plants, small cluster plants, and birds. Raspberry Pi 3 Model-B processor is used to implement this proposed algorithm. These performance metrics results show that the proposed detection approach is effective, more accurate, and suitable to different lake environments.