Evaluating Deep Convolutional Neural Networks for Material Classification
Determining the material category of a surface from an image is a demanding task in perception that is drawing increasing attention. Following the recent remarkable results achieved for image classification and object detection utilising Convolutional Neural Networks (CNNs), we empirically study material classification of everyday objects employing these techniques. More specifically, we conduct a rigorous evaluation of how state-of-the art CNN architectures compare on a common ground over widely used material databases. Experimental results on three challenging material databases show that the best performing CNN architectures can achieve up to 94.99\% mean average precision when classifying materials.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGeneral Classificationimage-classificationImage ClassificationMaterial Classificationobject-detectionObject DetectionSimilar Papers 제목 키워드 기반
Transfer Learning for Material Classification using Convolutional Networks
Material classification in natural settings is a challenge due to complex interplay of geometry, reflectance properties, and illumination. Previous work on material classification relies strongly on hand-engineered featu…
ClassificationDescriptiveGeneral ClassificationMaterial Classification+3One Patch is All You Need: Joint Surface Material Reconstruction and Classification from Minimal Visual Cues
Understanding material surfaces from sparse visual cues is critical for applications in robotics, simulation, and material perception. However, most existing methods rely on dense or full-scene observations, limiting the…
Spatial ReasoningMaterial Classification in the Wild: Do Synthesized Training Data Generalise Better than Real-World Training Data?
We question the dominant role of real-world training images in the field of material classification by investigating whether synthesized data can generalise more effectively than real-world data. Experimental results on …
General ClassificationMaterial ClassificationMaterial Recognition in the Wild with the Materials in Context Database
Recognizing materials in real-world images is a challenging task. Real-world materials have rich surface texture, geometry, lighting conditions, and clutter, which combine to make the problem particularly difficult. In t…
Material RecognitionSegmentationTowards a Safer and Sustainable Manufacturing Process: Material classification in Laser Cutting Using Deep Learning
Laser cutting is a widely adopted technology in material processing across various industries, but it generates a significant amount of dust, smoke, and aerosols during operation, posing a risk to both the environment an…