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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/53979

Title: 基於視覺之影像辨識於箱網養殖之應用
Application of Vision Based Target Recognition to Cage Aquaculture
Authors: Chen, Chao-Xun
陳朝勛
Contributors: NTOU:Department of Communications Navigation and Control Engineering
國立臺灣海洋大學:通訊與導航工程學系
Keywords: 無人機;影像辨識;神經網路;樹莓派
Drone;Image Recognition;Neural Network;Support Vector Machine (SVM);Raspberry Pi
Date: 2019
Issue Date: 2020-07-09T02:52:28Z
Abstract: 本研究將無人機應用場合設定於箱網養殖業中,主要目標分為環境檢測與箱網辨識兩種,並考量到機載處理器的運算能力、影像處理的複雜度與任務所需控制的元件數量等因素,將兩種任務分別使用不同的影像處理技術來辨識相應的目標物。第一項環境檢測,使用Hough Transform辨識簡易目標物(純色圓形),並計算其與無人機實際的水平距離來進行微調及收放感測器。第二項為箱網辨識,分別使用Support Vector Machine (SVM)與神經網路來辨識箱網,並比較兩者辨識效果。本論文將無人機、攝影機、樹莓派開發板與伺服馬達整合,搭配GPS的自動巡航功能,控制無人機進行微調後在準確的地點控制伺服馬達收放感測器來達到環境檢測或箱網辨識的功能,以達到輔助箱網養殖業者之目的。
This study applies drones in the cage farming industry and sets two goals that drones need to complete. One is environment detection and another is cage recognition. Considering computing power of the on-board processor and computation of the image processing, we use different image recognition theory to recognize different target. In environment detection, Hough Transform is used to recognize simple target (single color circle), and get the horizontal distance of the drone and target from image to adjust the drone and drop the sensor. In cage recognition, Support Vector Machine (SVM) and neural network are used to recognize the cage, and comparison of these recognition theories is given. This study integrates the drone, camera, Raspberry Pi and servo motor, and uses basic sensors and cruising function with GPS. The drone can fly to the destination, recognize the target and do the mission which is set previously.
URI: http://ethesys.lib.ntou.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=G0010667007.id
http://ntour.ntou.edu.tw:8080/ir/handle/987654321/53979
Appears in Collections:[通訊與導航工程學系] 博碩士論文

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