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A simple and effective digital imaging approach for tuna fish length measurement compatible with fishing operations

金枪鱼 垂钓 鱼类测量 存货评估 计算机科学 计算机视觉 渔业 人工智能 数学 生物
作者
Chia‐Wei Hsieh,Han-Yu Chang,Fei-Hung Chen,Jhao-Huei Liou,Shui-Kai Chang,Ta‐Te Lin
出处
期刊:Computers and Electronics in Agriculture [Elsevier BV]
卷期号:75 (1): 44-51 被引量:34
标识
DOI:10.1016/j.compag.2010.09.009
摘要

Fish length measurement of commercial catches is an important data input to many fish stock assessments to appraise stock status and to provide management advice for sustainable fisheries. However, as the process of measurement is usually laborious or complex, it is difficult to operate on longline fishing vessels on deck. This study proposes an effective technique for measuring a tuna's snout to fork length (SNFL) in digital images or photographs taken on the deck of tuna fishing vessels by observers using hand-held camera. Photographic factors, such as direction angle, top-view angle, and distance to fish body were examined in terms of estimation error. Hough transform (HT) and projective transform (PT) were applied to conduct line detection in automatic mode and to correct for projective distortion of the fish images, respectively. Experimental results showed that the estimation error was within 5% when the image was taken with a direction angle of 315–0° or 135–225°, and with a top-view angle of more than 45°. The PT correction reduced the estimation error especially in small top-view angle samples, with an average improvement of 64% and 48% in manual mode and in automatic mode, respectively. The proposed method was validated with the results showing that, after 30 min of practice, an operator can successfully make a measurement within a minute while the average (±std. dev.) estimation error was 4.5 ± 4.4% using the developed software program. With slight modification, the developed approach could be adapted to various applications concerning size measurement of plants or animals using photometric approach.

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