Industrial Technology Research Institute

Wei-Hao Lai

Pei-Jung Liang

Shih-Ming Chen

Peter Chondro

Chih-Kang Peng

Tse-Min Chen

Shanq-Jang Ruan

Cheng-Yi Lin

The disclosure is directed to an object detection method using a CNN model and an object detection apparatus thereof. In an aspect, the object detection method includes generating a sensor data; processing the sensor data by using a first object detection algorithm to generate a first object detection result; processing the first object detection result by using a plurality of stages of sparse update mapping algorithm to generate a plurality of stages of updated first object detection result; processing a first stage of the stages of updated first object detection result by using a plurality of stages of spatial pooling algorithm between each of stages of sparse update mapping algorithm; executing a plurality of stages of deep convolution layer algorithm to extract a plurality of feature results; and performing a detection prediction based on a last-stage feature result.

Object detection method using CNN model and object detection apparatus using the same

In one of the exemplary embodiments, the disclosure is directed to a depth estimation apparatus including a first type of sensor for generating a first sensor data; a second type of sensor for generating a second sensor data; and a processor coupled to the first type of sensor and the second type of sensor and configured at least for: processing the first sensor data by using two stage segmentation algorithms to generate a first segmentation result and a second segmentation result; synchronizing parameters of the first segmentation result and parameters of the second sensor data to generate a synchronized second sensor data; fusing the first segmentation result, the synchronized second sensor data, and the second segmentation result by using two stage depth estimation algorithms to generate a first depth result and a second depth result.

Depth estimation apparatus, autonomous vehicle using the same, and depth estimation method thereof

An object detecting device, an object detecting method and a non-transitory computer-readable medium are provided. The object detecting method includes the following steps: A classifier generates a current color image and a current gray scale image. The classifier generates an initial characteristic pattern from the current color image via a neural network algorithm. The classifier adjusts a current dimension of the initial characteristic pattern to generate an adjusted characteristic pattern according to a gray scale image dimension of the current gray scale image. The classifier concatenates the adjusted characteristic pattern and the current gray scale image to calculate a class confidence. The classifier determines whether the class confidence is larger than a confidence threshold, and outputs a current classification result if the class confidence is larger than the confidence threshold. A storage device stories the current classification result.

Object detecting device, object detecting method and non-transitory computer-readable medium

A protective material is provided, which includes a polymer polymerized from a monomer and a plurality of silica particles. The monomer includes acrylamide with a formula of wherein each R is independently H or Calkyl group, and R' is Calkylene group. The silica particles and the polymer have a weight ratio of 1.5:1 to 4:1.

Protective material and protective structure and protective method

A shear thickening formulation and composite material employing the same are provided. The shear thickening formulation includes inorganic particles and polyethylene glycol. The inorganic particles and the polyethylene glycol have a weight ratio of 3 to 4. The inorganic particles can be silica, aluminum oxide, silicon carbide, nano diamond, or a combination thereof.

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Taipei, Taiwan

Wei-Hao Lai