Environmental monitoring guides conservation and is particularly important for aquatic habitats which are heavily impacted by human activities. Underwater cameras and uncrewed devices monitor aquatic wildlife, but manual processing of footage is a significant bottleneck to rapid data processing and dissemination of results. Deep learning has emerged as a solution, but its ability to accurately detect animals across habitat types and locations is largely untested for coastal environments. Here, we produce five deep learning models using an object detection framework to detect an ecologically important fish, luderick (Girella tricuspidata). We trained two models on footage from single habitats (seagrass or reef) and three on footage from both habitats. All models were subjected to tests from both habitat types. Models performed well on test data from the same habitat type (object detection measure: mAP50: 91.7 and 86.9% performance for seagrass and reef, respectively) but poorly on test sets from a different habitat type (73.3 and 58.4%, respectively). The model trained on a combination of both habitats produced the highest object detection results for both tests (an average of 92.4 and 87.8%, respectively). The ability of the combination trained models to correctly estimate the ecological abundance metric, MaxN, showed similar patterns. The findings demonstrate that deep learning models extract ecologically useful information from video footage accurately and consistently and can perform across habitat types when trained on footage from the variety of habitat types.

环境监测指导保护工作，对于受人类活动严重影响的水生生境尤其重要。水下摄像头和未拧紧的设备可以监视水生野生生物，但是手动处理素材是快速数据处理和结果发布的重大瓶颈。深度学习已经成为一种解决方案，但是它在跨栖息地类型和位置准确检测动物的能力在沿海环境中尚未经过测试。在这里，我们使用对象检测框架生成五种深度学习模型，以检测具有生态学意义的鱼类luderick（Girella tricuspidata）。我们针对来自单个栖息地（海草或礁石）的镜头训练了两个模型，针对来自两个栖息地的镜头训练了三个模型。所有模型都经过了两种栖息地类型的测试。在来自相同栖息地类型的测试数据上，模型表现良好（目标检测指标：mAP50：海草和珊瑚礁的性能分别为91.7和86.9％），但是在来自不同栖息地类型的测试集上，模型表现不佳（分别为73.3和58.4％）。在两个生境的组合上训练的模型在两个测试中产生的物体检测结果最高（分别为92.4和87.8％）。组合训练模型正确估计生态丰度指标MaxN的能力显示出相似的模式。