Automated detection of whales in aerial imagery

自动检测航空图像中的鲸鱼

• 批准号: 567133-2021
• 负责人: Clausi, DavidDA
• 金额: $ 6.56万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765883
• 关键词: Automated; detection; whales; aerial; imagery

Remote sensing, or capturing and processing images of the earth's surface from satellite or aerial platforms, can efficiently allow scientific monitoring of vast earth regions. As an example, remote sensing is essential for monitoring whale populations. Whales are biologically and environmentally important to Canada and part of food security as well as culturally and economically important for Inuit. Although many images can be captured quickly, a year is required to manually process the 1000s of images aerially captured in a single whale field survey. Our research endeavors to use automated computer vision algorithms to expedite this process. This task is challenging due to infrequent observation of whales, the small size of the whale relative to the image size, and the often unclear whale appearance due to the unpredictable ocean appearance. Novel machine learning models for computer-aided processing and analysis of the large-volume complex images can be used to perform automated whale population counts, and thereby process survey image data more efficiently and consistently. Advancements will be made with respect to improved capture of visual cues to identify what could be a whale and context-aware methods using prior knowledge to distinguish whales from look-alikes. These new methods will facilitate the development of remote sensing software and analytic solutions for whale identification in field survey images. The methods can then also support other remote sensing target detection and classification tasks in ocean monitoring applications.

遥感，或从卫星或航空平台捕获和处理地球表面图像，可以有效地对地球广大地区进行科学监测。例如，遥感对监测鲸鱼数量至关重要。鲸鱼对加拿大具有重要的生物和环境意义，是食品安全的一部分，对因纽特人具有重要的文化和经济意义。虽然许多图像可以快速捕获，但需要一年的时间来手动处理在单个鲸鱼实地调查中空中捕获的数千张图像。我们的研究努力使用自动计算机视觉算法来加快这一过程。这项任务具有挑战性，因为对鲸鱼的观察很少，鲸鱼的尺寸相对于图像尺寸较小，而且由于海洋外观不可预测，鲸鱼的外观往往不清晰。用于计算机辅助处理和分析大容量复杂图像的新型机器学习模型可用于执行自动鲸鱼种群计数，从而更有效和一致地处理调查图像数据。在改进视觉线索捕捉以识别什么可能是鲸鱼以及使用先验知识区分鲸鱼和同类的上下文感知方法方面，将取得进展。这些新方法将促进在野外调查图像中识别鲸鱼的遥感软件和分析解决方案的发展。这些方法还可以支持海洋监测应用中的其他遥感目标检测和分类任务。