SBIR Phase I: Online High Fidelity 3D Modeling of Produce Using Low Cost Sensors

这个小企业创新研究（SBIR）第一阶段项目调查了雇用高？通过结构光技术捕获的食品产品的分辨率，彩色，3D模型，以根据质量识别（ID）和分类。将确定在田间收获的实时限制下实现这一目标的可行性。目前需要解决的问题是，高保真建模需要控制校准和采集过程，昂贵的传感器硬件和耗时的全局优化算法，需要几分钟或几小时才能完成。该方法是研究新的处理算法来扩展二维超分辨率原理，并利用新的概率运动和传感器误差模型来实现精确的多帧配准和快速的全局寻优。这项研究将开发新的方法，在更小的相似的集群上运行。像素和利用3D概率占用映射，同时利用成像和几何特征来减少计算时间和噪声。如果可以实现，好处包括开发一种具有成本效益和功能丰富的先进数据建模技术，该技术可以集成到产品收集机器中，并用于根据外观缺陷经济有效地分割单个食品。预计这将为中小型农场企业降低成本并增加收入。该项目的广泛影响/商业潜力涉及两个方面：1)国内农产品养殖的关键差距，这阻碍了小农与大型企业农场在效率、质量控制和产品定价方面的竞争；2)传感器系统和技术更广泛领域的真正进步。虽然第一阶段的目标是为水果和蔬菜提供分布式、低成本的建模和分拣应用，但该技术应该通过改善肉类、海鲜、奶酪甚至烘焙食品等的配送和分拣，为更广泛的消费食品市场提供类似的好处，同时还可以推动许多其他领域受益于良好的ID和分拣技术。至少，这种具有成本效益的自动化分拣能力预计将使小农场的收入增加至少25%，并通过其战略合作伙伴为报价方提供蓬勃发展的全球业务。此外，这种经济实惠、快速的3D数据建模和检测技术可以准确地对几何缺陷进行分类，预计将在数据建模和检测领域产生更广泛的影响。

This Small Business Innovation Research (SBIR) Phase I project investigates the feasibility of employing high?resolution, colorized, 3D models of food produce captured with structured light technology to identify (ID) and sort produce by quality. The feasibility of accomplishing this within the real-time constraints of in-field harvesting will be determined. The PROBLEM being addressed is that high fidelity modeling currently requires controlled calibration and acquisition processes, expensive sensor hardware and time consuming global optimization algorithms taking minutes, or hours, to complete. The proposed APPROACH is to research novel processing algorithms to extend 2D super-resolution principles and exploit new probabilistic motion and sensor error models to achieve precise multi-frame registration and fast global optimization. This research will exploit new methods which operate on smaller clusters of ?similar? pixels and leverage 3D probabilistic occupancy mappings, taking advantage of imaging and geometry features simultaneously to reduce computation time and noise. If achievable, the BENEFITS include development of a cost-effective and feature-rich advanced data modeling technology that can be integrated into produce collection machinery and used to cost-effectively segment individual food items based on cosmetic imperfections. This is expected to provide both reduced costs and increased revenues to small and medium farm enterprises.The broader impact/commercial potential of this project addresses two areas: 1) a critical gap within domestic produce farming which prevents small farmers from competing with large, corporate enterprise farms in terms of efficiency, quality control and product pricing and 2) a very real advance in the broader area of sensor systems and technology. Although a distributed, low cost modeling and sorting application for fruits and vegetables is targeted in Phase I, the technology should provide similar benefits to the broader consumer foods market by improving the distribution and sorting of meats, seafood, cheeses or even baked goods, and the like, while also advancing many other areas that can benefit from good ID and sorting technology. At the very least, this cost effective, automated, sorting capability is expected to increase small farm revenues by at least 25% and provide the offeror with a burgeoning, worldwide business through its strategic partners. In addition, such an affordable, and fast, 3D data modeling and inspection technology for accurately categorizing geometric defects can be expected to have a much broader impact within the field of data modeling and inspection.