Data from: Towards a fully automated underwater census for fish assemblages in the Mediterranean Sea

Assessing underwater biodiversity is labour-intensive and costly, but is crucial for measuring the extent of the decline in local fish stock. In most cases, Underwater Visual Census (UVC) is the preferred method, however this can be costly in terms of human effort and is limited by meteorological and logistical factors. Advances in technology allows the utilisation of more autonomous video recording methods (i.e. Remote Operated Vehicles (ROV)) which addresses these limitations. This study used a transect-wise UVC coupled with diver operated videos (DOV). For the video analysis, a comprehensive fully automated pipeline was developed to extract frames from DOV and perform colour correction. This pipeline integrates a YOLO-based model to detect 20 Mediterranean fish species and validate the presence or absence of each species within individual transects. This study was conducted to evaluate the feasibility of using video-based methods for UVC with minimal human-input. The result of automated video analysis were in agreement with manual video counting, validating the autonomous and bias-free procedure for video assessment. In conclusion, utilising a minimal-human-input video method liberates the data acquisition from limiting factors (i.e. meteorological and logistical) and automation of this video analysis significantly reduces the labour and time required. For future fieldwork campaigns, the video data collection protocol needs to be modified to better resemble traditional UVC and enhance this acquisition method.水下生物多样性评估属于劳动密集型且成本高昂的工作，但对于衡量当地鱼类种群的衰退程度至关重要。多数情况下，水下视觉普查（Underwater Visual Census, UVC）是首选方法，但该方法的人力投入成本高昂，且受气象与后勤因素限制。技术进步使得更多自主式视频录制手段得以应用，例如遥控水下载具（Remote Operated Vehicles, ROV），这一方式可解决上述局限。本研究采用样带式水下视觉普查结合潜水员操作视频（Diver Operated Videos, DOV）的方案。针对视频分析环节，本研究开发了一套完整的全自动处理流程，可从潜水员操作视频中提取帧并完成色彩校正。该流程集成了基于YOLO的模型，用于检测20种地中海鱼类，并验证各物种在对应样带中的存在与否。本研究旨在评估在最小人力投入下，采用视频类方法开展水下视觉普查的可行性。全自动视频分析的结果与人工视频计数结果相符，验证了该视频评估流程的自主性与无偏性。综上，采用低人力投入的视频采集方法，可使数据采集摆脱气象与后勤等限制因素，且该视频分析流程的自动化可大幅降低所需人力与时间成本。针对未来的野外作业计划，需对视频数据采集方案进行优化调整，使其更贴近传统水下视觉普查流程，从而进一步完善该采集方法。