Department of Electrical Engineering, National University of Tainan, Tainan 70005, Taiwan.
Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan 33302, Taiwan.
Sensors (Basel). 2022 Jul 25;22(15):5545. doi: 10.3390/s22155545.
The proportion of pet keeping has increased significantly. According to the survey results of Business Next, the proportion of Taiwan families keeping pets was 70% in 2020. Among them, the total number of fish pets was close to 33% of the overall pet proportion. Therefore, aquarium pets have become indispensable companions for families. At present, many studies have discussed intelligent aquarium systems. Through image recognition based on visual sensors, we may be able to detect and interpret the physiological status of the fish according to their physiological appearance. In this way, it can help to notify the owner as soon as possible to treat the fish or isolate them individually, so as to avoid the spread of infection. However, most aquarium pets are kept in groups. Traditional image recognition technologies often fail to recognize each fish's physiological states precisely because of fish swimming behaviors, such as grouping swimming, shading with each other, flipping over, and so on. In view of this, this paper tries to address such problems and then proposes a practical scheme, which includes three phases. Specifically, the first phase tries to enhance the image recognition model for small features based on the prioritizing rules, thus improving the instant recognition capability. Then, the second phase exploits a designed fish-ID tracking mechanism and analyzes the physiological state of the same fish-ID through coherent frames, which can avoid temporal misidentification. Finally, the third phase leverages a fish-ID correction mechanism, which can detect and correct their IDs periodically and dynamically to avoid tracking confusion, and thus potentially improve the recognition accuracy. According to the experiment results, it was verified that our scheme has better recognition performance. The best accuracy and correctness ratio can reach up to 94.9% and 92.67%, which are improved at least 8.41% and 26.95%, respectively, as compared with the existing schemes.
养宠物的比例显著增加。根据 Business Next 的调查结果，2020 年台湾家庭饲养宠物的比例为 70%。其中，鱼类宠物的总数接近整体宠物比例的 33%。因此，水族宠物已成为家庭不可或缺的伴侣。目前，许多研究都探讨了智能水族箱系统。通过基于视觉传感器的图像识别，我们可以根据鱼类的生理外观来检测和解释其生理状态。这样，一旦发现异常，就可以及时通知主人对鱼类进行治疗或单独隔离，从而避免感染的传播。然而，大多数水族宠物都是成群饲养的。由于鱼类的游动行为，如成群游动、相互遮挡、翻转等，传统的图像识别技术往往无法准确识别每条鱼的生理状态。针对这一问题，本文试图提出一种实用的解决方案，该方案包括三个阶段。具体来说，第一阶段尝试基于优先级规则增强小特征的图像识别模型，从而提高即时识别能力。然后，第二阶段利用设计的鱼类 ID 跟踪机制和连贯帧分析同一鱼类 ID 的生理状态，从而避免时间上的误识别。最后，第三阶段利用鱼类 ID 校正机制，周期性地动态检测和校正鱼类 ID，以避免跟踪混乱，从而潜在地提高识别精度。实验结果验证了我们的方案具有更好的识别性能。最佳的准确性和正确性比例分别高达 94.9%和 92.67%，与现有方案相比，分别至少提高了 8.41%和 26.95%。
