3D data analysis capable of recognizing and classifying objects and spaceDeep Mesh

3D data analysis that classifies objects, identifies parts, and recognizes an entire space based on images and video captured by camera.
Using Rist’s original neural network, the AI recognizes objects by converting images and video to point cloud data.
Available for surveys using self-flying drone cameras, laser radar object recognition, behavior analysis, and more.

Three Technologies Used in 3D Data Analysis

Classification

The technology that recognizes and classifies 3D objects. It can differentiate between types and assign qualitative rankings.

Part Segmentation

A technology that automatically distinguishes between partial sections of the target. Capable of extrapolating information, etc. on required parts.

Semantic Segmentation

Objects are extracted from an image, and the AI recognizes the boundary outline for each object. With aerial 3D landscape data, the AI can analyze information about the area.

case.01Optimizing performance using a
self-flying drone and AI

Toda Corporation

We developed the drone camera “Blast Eye” for viewing blast sites in real time, and developed “Blast AI” to study the observations and judgments of experts in order to determine the quality of a site.

When construction company Toda Corporation and Rist Co., Ltd. excavate mountain tunnels by blasting through the rock, the next blasting pattern is determined depending on the shape of the blasting stone (blasting stepping stone) from the last blast. Up until now, those judgments were made by experienced tunnel technicians, but we developed the “Blast Eye / AI” that automatically judges the quality of blasts by studying and learning from those experts’ decisions.

Blast Eye

Stable flight even in long, narrow stretches where GPS cannot be used.

Blast AI

Studies various simulated blasting stone shapes and learns to determine blast quality.

case.02Utilizing 3D laser radar AI

Real-time 3D information acquisition and object recognition, behavior analysis, and shape recognition technology using 3D-LIDAR

Makes a real time measurement of the amount of time it takes for the LIDAR laser to be reflected back from the target object, and obtains a 3D image of the object.

Example of motion detection using 3D laser radar

Simultaneously recognizes both the presence of pedestrians and the motion vector (detecting the direction in which it is moving and its speed)

https://www.konicaminolta.com/jp-ja/future/3dlr/
https://special.nikkeibp.co.jp/atcl/NBO/16/010500001/

Estimating the distance to low-reflectivity objects with deep learning

Improving distance measurement accuracy for low-reflectivity objects, which is a challenge for 3D-LIDAR

Black cars are recognized, distance information is supplemented, and the exact distance is calculated.

Installation Process

  1. 01Examination and Analysis

    The installation method is planned according to the end goal, whether it is new construction, or implementation into the customer’s products or existing production line.
    Feasibility will be examined based on a rough consultation that could occur via email, videoconference, or face to face. Feel free to get in touch with us and start the process.

  2. 02System Design

    After collecting data specific to the customer’s worksite, we devise an optimal learning algorithm, and then go through prototype development before entering main development and delivering the product.When necessary, we collaborate with companies in various fields to design and develop hardware than can handle special inspections.

  3. 03Result Measurement and Verification

    With the constructed line in action, we measure and verify result data, fine-tuning the AI program to optimize its performance.

  4. 04Beginning System Operation

    After installation, we continue to support customers through maintenance inspections and additional AI learning.
    We also offer consultations for more advanced inspections.