SBIR Phase I: Deep Learning Classifier Using Hyper Spectral Imaging for Automated Processing of Fish Fillets

SBIR 第一阶段：使用高光谱成像的深度学习分类器自动处理鱼片

• 批准号: 1947633
• 负责人: KRISTJAN INGVARSSON
• 金额: $ 22.47万
• 依托单位: OCELAND LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-15 至 2021-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1947633&HistoricalAwards=false
• 关键词: SBIR; Phase; Deep; Learning; Classifier

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be to advance the development of solutions for the fish processing industry. Current fish fillet preparation depends highly on visual inspection and manual trimming by trained technicians to identify and remove blemishes and food-borne parasites, which remain a significant cost of seafood preparation. Preparation of seafood by hand also poses significant health risks for consumers. The presence of human technicians means significant portions of seafood processing must be performed at room temperature, increasing the risk of bacterial infection and enzymatic degradation. This decreases the freshness and quality of the final product, while increasing chances of foodborne illness. Additionally, the manual detection rate for nematode infections can be as low as 50% under industrial conditions due to the small size of the infections and the potential depth in the fish. The proposed automated hyperspectral imaging (HSI)-based platform addresses these issues by (1) fully automating the complete fish processing supply/value chain, (2) obviating industry dependence on manual inspection and (3) being much less error-prone and more precise than manual inspection and trimming, reducing lost yield and promoting safer products for consumers. This Small Business Innovation Research (SBIR) Phase I project will advance the development of a hyperspectral imaging (HSI) system for the automatic processing and remediation of fish fillets. HSI has already been validated in the detection of anatomical features of fish fillets and detection of harmful parasites like nematodes. However, its use has not been validated on an industrial scale. The proposed research will focus on: (1) compilation and annotation of a fish fillet data set, (2) training and validation of deep learning algorithms to detect fish fillet center line and nematode infestations, and (3) assessing the feasibility of using a state-of-the-art robotic actuator and customizing it for automatic nematode removal with the company's novel algorithms. The result will be a computer vision platform capable of recognizing nematodes/other foodborne parasites, detecting blemishes, blood spots, and other undesirable cosmetic marks, as well as conducting precision removal of identified parasites and blemishes without excess loss of fish meat.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项小企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力将是推动鱼类加工业解决方案的发展。目前的鱼片制备高度依赖于目视检查和由训练有素的技术人员手工修剪，以识别和去除瑕疵和食源性寄生虫，这仍然是海产品制备的重要成本。手工制备海产品也会给消费者带来重大的健康风险。人类技术人员的存在意味着海鲜加工的很大一部分必须在室温下进行，这增加了细菌感染和酶降解的风险。这降低了最终产品的新鲜度和质量，同时增加了食源性疾病的机会。此外，在工业条件下，人工对线虫感染的检出率可低至50%，因为感染的规模很小，而且鱼体内的潜在深度很大。提出的基于自动化高光谱成像（HSI）的平台通过以下方式解决了这些问题：(1)完全自动化完整的鱼类加工供应链/价值链；(2)消除了行业对人工检查的依赖；(3)比人工检查和修剪更不容易出错，更精确，减少了产量损失，为消费者提供了更安全的产品。这项小型企业创新研究（SBIR）第一阶段项目将推进用于鱼片自动处理和修复的高光谱成像（HSI）系统的开发。HSI已经在检测鱼片的解剖特征和检测线虫等有害寄生虫方面得到了验证。然而，它的使用尚未在工业规模上得到验证。拟议的研究将集中在：(1)鱼片数据集的编译和注释，(2)深度学习算法的训练和验证，以检测鱼片中心线和线虫感染，以及(3)评估使用最先进的机器人执行器的可行性，并根据公司的新算法定制其用于自动清除线虫。结果将是一个计算机视觉平台，能够识别线虫/其他食源性寄生虫，检测瑕疵，血斑和其他不受欢迎的化妆品标记，以及在不过量损失鱼肉的情况下精确去除已识别的寄生虫和瑕疵。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。