论文标题
使用营养和涟漪行为的特征自动控制鱼类进食机
Automatic Controlling Fish Feeding Machine using Feature Extraction of Nutriment and Ripple Behavior
论文作者
论文摘要
控制鱼进料机是具有挑战性的问题，因为经验丰富的渔民可以根据假设充分控制。为了构建合理应用的强大方法，我们分别使用回归和纹理特征分别使用计数营养和估算涟漪行为的组合，根据计算机视觉自动控制鱼类进料机。为了计算营养数量，我们采用对象检测和跟踪方法来确认迁移到海面的营养。最近，对象跟踪是计算机视觉中的积极研究和具有挑战性的问题。不幸的是，在具有更多外观生物的水产养殖场中，具有密度和复杂关系的多个小物体的可靠跟踪方法是未解决的问题。根据营养和涟漪行为的数量，我们可以控制在实际环境中始终如一地表现良好的鱼类进食机。提出的方法提出了通过激活图和连锁行为的质地特征自动控制鱼类进食的一致性。与其他方法相比，我们的跟踪方法可以精确跟踪下一帧的营养。基于计算时间，提出的方法达到3.86 fps，而其他方法的支出低于1.93 fps。定量评估可以承诺，提出的方法对于广泛适用于真实环境的水产养殖养鱼场很有价值。
Controlling fish feeding machine is challenging problem because experienced fishermen can adequately control based on assumption. To build robust method for reasonable application, we propose automatic controlling fish feeding machine based on computer vision using combination of counting nutriments and estimating ripple behavior using regression and textural feature, respectively. To count number of nutriments, we apply object detection and tracking methods to acknowledge the nutriments moving to sea surface. Recently, object tracking is active research and challenging problem in computer vision. Unfortunately, the robust tracking method for multiple small objects with dense and complex relationships is unsolved problem in aquaculture field with more appearance creatures. Based on the number of nutriments and ripple behavior, we can control fish feeding machine which consistently performs well in real environment. Proposed method presents the agreement for automatic controlling fish feeding by the activation graphs and textural feature of ripple behavior. Our tracking method can precisely track the nutriments in next frame comparing with other methods. Based on computational time, proposed method reaches 3.86 fps while other methods spend lower than 1.93 fps. Quantitative evaluation can promise that proposed method is valuable for aquaculture fish farm with widely applied to real environment.
