A Model that Predicts the Material Recognition Performance of Thermal Tactile Sensing

Tactile sensing can enable a robot to infer properties of its surroundings, such as the material of an object. Heat transfer based sensing can be used for material recognition due to differences in the thermal properties of materials. While data-driven methods have shown promise for this recognition problem, many factors can influence performance, including sensor noise, the initial temperatures of the sensor and the object, the thermal effusivities of the materials, and the duration of contact. We present a physics-based mathematical model that predicts material recognition performance given these factors. Our model uses semi-infinite solids and a statistical method to calculate an F1 score for the binary material recognition. We evaluated our method using simulated contact with 69 materials and data collected by a real robot with 12 materials. Our model predicted the material recognition performance of support vector machine (SVM) with 96% accuracy for the simulated data, with 92% accuracy for real-world data with constant initial sensor temperatures, and with 91% accuracy for real-world data with varied initial sensor temperatures. Using our model, we also provide insight into the roles of various factors on recognition performance, such as the temperature difference between the sensor and the object. Overall, our results suggest that our model could be used to help design better thermal sensors for robots and enable robots to use them more effectively.Comment: This article is currently under review for possible publicatio

PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentation

Unsupervised pixel-level defective region segmentation is an important task in image-based anomaly detection for various industrial applications. The state-of-the-art methods have their own advantages and limitations: matrix-decomposition-based methods are robust to noise but lack complex background image modeling capability; representation-based methods are good at defective region localization but lack accuracy in defective region shape contour extraction; reconstruction-based methods detected defective region match well with the ground truth defective region shape contour but are noisy. To combine the best of both worlds, we present an unsupervised patch autoencoder based deep image decomposition (PAEDID) method for defective region segmentation. In the training stage, we learn the common background as a deep image prior by a patch autoencoder (PAE) network. In the inference stage, we formulate anomaly detection as an image decomposition problem with the deep image prior and domain-specific regularizations. By adopting the proposed approach, the defective regions in the image can be accurately extracted in an unsupervised fashion. We demonstrate the effectiveness of the PAEDID method in simulation studies and an industrial dataset in the case study

VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON

Despite progress in vision-based inspection algorithms, real-world industrial challenges -- specifically in data availability, quality, and complex production requirements -- often remain under-addressed. We introduce the VISION Datasets, a diverse collection of 14 industrial inspection datasets, uniquely poised to meet these challenges. Unlike previous datasets, VISION brings versatility to defect detection, offering annotation masks across all splits and catering to various detection methodologies. Our datasets also feature instance-segmentation annotation, enabling precise defect identification. With a total of 18k images encompassing 44 defect types, VISION strives to mirror a wide range of real-world production scenarios. By supporting two ongoing challenge competitions on the VISION Datasets, we hope to foster further advancements in vision-based industrial inspection

Towards Material Classification of Scenes Using Active Thermography

By briefly heating the local environment with a heat lamp and observing what happens with a thermal camera, robots could potentially infer properties of their surroundings. However, this form of active thermography introduces large signal variations compared to traditional active thermography, which has typically been used to characterize small regions of materials in carefully controlled settings. We demonstrate that a data-driven approach with modern machine learning methods can be used to classify material samples over relatively large surface areas and variable distances. We also introduce the use of z-normalization to improve material classification and reduce variation due to distance and heating intensity. Our best performing algorithm achieved an overall accuracy of 77.7% for multi-class classification among 12 materials placed at varying distances (20 cm, 30 cm, and 40 cm). The observations were made for 5 seconds with 1s of heating and 4s of cooling. We also provide a demonstration of performance with a multi-material scene.Undergraduat

Effect of submerged macrophytes on metal and metalloid concentrations in sediments and water of the Yunnan Plateau lakes in China

Purpose Submerged macrophytes have an ability to absorb metals and metalloids either from the sediments via the roots, from the water by the leaves, or from both sources. The objectives of this study were (1) to test the hypothesis that metal and metalloid concentrations in water and sediments from sampling sites with submerged macrophytes are significantly lower than those from sampling sites without submerged macrophytes, (2) to explore the accumulation potential for metals and metalloids of different submerged macrophyte species, and (3) to discuss the relationships among submerged macrophytes, water, and sediments in the Yunnan Plateau lakes. Materials and methods Twenty Yunnan Plateau lakes with different trophic levels were selected. Concentrations of 16 metals and metalloids (Al, As, Ba, Cd, Co, Cr, Cu, Fe, Li, Mn, Mo, Ni, Pb, Se, Sr, and Zn) in submerged macrophytes, water, and sediments were determined by using ICP-AES. Relationships among metal and metalloid concentrations in water, sediments, and submerged macrophytes were carried out by Pearson correlation analysis. The enrichment factor was calculated as the ratio between the concentration of metals and metalloids in a sediment sample and the soil background value. Results and discussion No significant differences were found in metal and metalloid concentrations in water and sediments between sampling sites with submerged macrophytes and sampling sites without submerged macrophytes. Moreover, lake water and sediments were mainly contaminated by As, Cr, and Pb. Potamogeton distinctus is a hyperaccumulator of Fe according to the threshold value for Fe hyperaccumulation. Many significantly positive correlations were found among metals and metalloids in submerged macrophytes due to co-accumulation. We found significant correlation between Cr in submerged macrophytes and Cr in water, and strong positive correlations between As, Cd, and Cu in submerged macrophytes and As, Cd, and Cu in corresponding sediments in the Yunnan Plateau lakes. Conclusions Submerged macrophytes have no significant effects on metal and metalloid concentrations in sediments and water in all the 20 Yunnan Plateau lakes in the study. However, further studies are necessary to understand the interactions of metals and metalloids in submerged macrophytes, water, and sediments