Introduce a new dynamic classifying algorithm in this paper to recognize and monitor fish activity to simultaneously better comprehend their synapomorphies. The pre-trained Mask Regional Convolutional Neural Network (Mask-R-CNN) is trained using a set of test models extracted from recorded video recording. The approach suggested subsequently yields well-enhanced feature vectors. The system’s automatic fish detection and tracking capabilities are improved, enhancing underwater investigation for supervising ecological biodiversity. The publicly accessible field-truth dataset assesses recall, F1-score, and classification and tracking precision. Utilizing current tracking R-CNN algorithms like Lowest Output Sum of Siamese Mask (SiamMask), Sequential Non-Maximum Suppression (Seq-NMS), and Squared Errors (MOSSE), comparative performance testing is conducted. In comparison to Siam-Mask (84%), Seq-NMS (78%) and MOSSE (75%), more than 120 of 170 specific bream were detected using the pre-trained Mask-R-CNN of the proposed algorithm (87%). This pre-trained Mask R-CNN system was used in the evaluation, and it was discovered that detection and tracking accuracy had increased significantly. This suggests that the ocean ecosystem could benefit from applying the proposed approach for management of ecology.

本文介绍了一种新的动态分类算法来识别和监测鱼类活动，同时更好地理解它们的突触形态。预训练的 Mask 区域卷积神经网络 (Mask-R-CNN) 使用从录制的视频记录中提取的一组测试模型进行训练。建议的方法随后会产生增强的特征向量。该系统的鱼类自动检测和跟踪能力得到提高，加强了水下调查以监督生态生物多样性。可公开访问的现场实况数据集评估召回率、F1 分数以及分类和跟踪精度。利用当前的跟踪 R-CNN 算法，如连体掩码的最低输出和 (SiamMask)、顺序非最大抑制 (Seq-NMS) 和平方误差 (MOSSE)，进行比较性能测试。与 Siam-Mask (84%)、Seq-NMS (78%) 和 MOSSE (75%) 相比，使用所提出算法的预训练 Mask-R-CNN 检测到 170 条特定鲷鱼中的 120 多条 (87 %)。这个预训练的Mask R-CNN系统被用于评估，发现检测和跟踪精度有了显着提高。这表明海洋生态系统可以受益于应用所提议的生态管理方法。