Photographic identification is an essential research and management tool for marine mammal scientists. However, manual identification of individuals is time-consuming. To shorten processing times, we developed finFindR, an open-source application that uses a series of neural networks to autonomously locate dorsal fins in unedited field images, quantify an individual's unique fin characteristics, and match them to an existing photograph catalog. During a blind test comparing manual searching to finFindR for common bottlenose dolphin (Tursiops Tursiops truncatus) photographs, experienced photo-identification technicians achieved similar match rates but examined an order of magnitude fewer photographs using finFindR (an average of 10 required with finFindR versus 124 with manual search). In those tests, the correct identity was ranked in the first position in 88% of cases and was within the top 50 ranked positions in 97% of cases. Our observations suggest that finFindR's matching capabilities are robust to moderate variation in image quality and fin distinctiveness. Importantly, finFindR allows users to build a catalog of known individuals through time and match an unlimited number of individuals instead of being restricted to a predefined set. finFindR's convolutional neural networks could be re-trained to identify members of many marine mammal species without altering finFindR's inherent structure.

中文翻译：

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finFindR：使用残差卷积神经网络自动识别和识别海洋哺乳动物背鳍

照相鉴定是海洋哺乳动物科学家必不可少的研究和管理工具。然而，人工识别个体是耗时的。为了缩短处理时间，我们开发了finFindR，这是一个开源应用程序，它使用一系列神经网络在未经编辑的野外图像中自主定位背鳍，量化个体独特的鳍特征，并将它们与现有的照片目录相匹配。在将手动搜索与finFindR对常见宽吻海豚 ( Tursiops Tursiops truncatus ) 照片进行比较的盲测期间，经验丰富的照片识别技术人员实现了相似的匹配率，但使用finFindR检查的照片数量减少了一个数量级（ finFindR平均需要 10 个，而手动搜索需要 124 个）。在这些测试中，正确身份在 88% 的案例中排名第一，在 97% 的案例中位于前 50 位。我们的观察表明，finFindR 的匹配能力对于图像质量和鳍独特性的适度变化是稳健的。重要的是，finFindR允许用户通过时间构建已知个人的目录并匹配无限数量的个人，而不是被限制在预定义的集合中。finFindR的卷积神经网络可以重新训练以识别许多海洋哺乳动物物种的成员，而不会改变finFindR的固有结构。