Deep Capture is the industrial vision software in deep learning. the most powerful image analysis software on the industrial market. It enables detection, analysis, and classification of objects. It is used for complex inspection issues that traditional systems cannot handle. industrial vision that traditional systems cannot address (large variety of references, objects with high light reflection). The software operates with supervised learning ( deep learning), using labeled images of the targeted areas. It works on the best GPUs on the market, we are able to detect and process images in real-time with frame rates exceeding 60 fps per camera.
Deep learning allows, based on a dataset of images to detect, to generalize the detection of elements not previously learned. In other words, our Deep Capture industrial vision system is capable of detecting objects with a high variability in shapes, textures, dimensions, and more. Based on a “hypermodel” created by our teams, the software allows defect detection across a wide variety of references without the need for specific format changes (no camera position or lighting adjustments) and frees you from external lighting variations.
Deep Capture requires no specific skills in industrial vision or deep learning to be operated and has very few settings to adjust. Two phases of automatic calibration prior to production will allow the model to be incremented with images of defect-free objects, and if necessary, define the camera thresholds for each pass.
Industrial vision software using deep learning.
How Deep Capture Works
The use of deep learning technology is revolutionizing industrial vision, and our video shows you how it works schematically.
What are the advantages of industrial vision in deep learning?
The use of deep learning technology is revolutionizing industrial vision, and our video shows you how it works schematically.
Taking photos and simple, intuitive labeling with DEEP VISION for industrial vision.
Our industrial vision software operates using supervised learning. The system requires characterizing and annotating the objects of interest in the images. This preliminary step is carried out using our Deep Vision software, which allows for simple and intuitive creation of “bounding boxes” around the objects in order to accelerate and optimize the process. The labeling does not need to be highly precise.
Creation and training of the model on the most advanced neural networks and frameworks on the market (deep learning).
Our models are trained on proven architectures using standard datasets from computer vision. The model is compatible and trainable on Google’s artificial intelligence platform: TensorFlow.
Automatic calibration, advantage of industrial vision in deep learning
Automatic calibration, advantage of industrial vision in deep learning
The tool allows thresholding of detected images. This threshold corresponds to the system’s “confidence level” for the given image on a scale from 0 to 100 (100 being the maximum confidence level). Our industrial vision software in deep learning automatically calibrates the threshold per camera by passing N qualified negative objects (those without defects to detect).
Reinforcement of the model online through the use of deep learning in our industrial vision software.
Reinforcement of the model online through the use of deep learning in our industrial vision software.
A feature of the Deep Capture industrial vision software allows the end user to improve the existing model and system performance by automatically adding photos of both negative and positive products.
Ease of multicamera integration for the development and integration of your industrial vision system.
Ease of multicamera integration for the development and integration of your industrial vision system.
Our deep learning industrial vision software allows up to 8 cameras to operate on the same PC. Adding a new camera is done in a simple and intuitive way.
A real breakthrough for industrial vision: the deep learning decision support algorithm (SVM, filters on the number of frames).
A real breakthrough for industrial vision: the deep learning decision support algorithm (SVM, filters on the number of frames).
The capture and analysis of high-frequency images (approximately 60 fps per camera) has enabled us to implement a number of tools.
The tracker is the simplest example; it validates a detection only when it detects the same object in at least n consecutive images.
We have also developed a decision-support tool, which is even more effective as creating a deterministic decision algorithm becomes complex and challenging as the number of objects to be detected increases.
Our decision-support algorithm uses the output of our deep learning model to make its decision. If the detection model changes, all that is needed is to retrain this decision algorithm using the already available data, so that it automatically adjusts to the new model.
We also allow the user to modify certain parameters in real-time, even after the model has been trained:
- Accepted confidence threshold in detection,
- Number of consecutive images to consider a defect,
- Object overlap,
- Size of the image considered by the neural network,
- Camera settings…
Our deep learning industrial vision software is also capable of managing multiple detection models simultaneously, allowing it to adapt to unexpected situations, such as a significant change in products on a production line, for example.
The strengths of Deep Capture
ARTIFICIAL INTELLIGENCE
Thanks to deep learning machine vision, our Deep Capture software enables generalized detection based on supervised learning. In other words, it can detect elements with different appearances, shapes, and dimensions from what it has learned.
The system is, for example, very useful for detecting objects that are subject to interpretation.
REAL-TIME ANALYSIS
Our machine vision software enables near-real-time detection. We can achieve a framerate of around 60 fps per camera. This high framerate allows us to obtain multiple detection results for a single object, even in high-speed scenarios. Combined with decision support algorithms, this feature gives our Deep Capture software a high level of confidence in the quality of its predictions.
BEST PERFORMANCE
In industrial vision, the performance of an optical inspection system is measured on two axes: the true positive detection rate (defects, objects to be identified) and the false positive rate (false rejections). With Deep Capture, our deep learning-based software guarantees the best balance between these two rates. Thus, you choose one of the most reliable industrial vision software solutions on the market.
SIMPLE AND INTUITIVE
With the Deep Capture deep learning industrial vision software, the mechanical and software campaign changeover times are virtually non-existent. Additionally, the system is insensitive to lighting variations and therefore does not require any specific configuration in this regard. Our next-generation industrial vision tool is very user-friendly and requires no specific expertise in deep learning.
TURN-KEY
Our engineers specialized in machine vision take care of the various phases of software development. From photo labeling to configuring the neural network and integrating the system on-site, the Deep Capture team is by your side. We also have the skills in mechanics, electrical engineering, and automation, allowing us to design the entire machine.
Deep learning industrial vision
What is it? Why is it more efficient?
Deep learning (or deep learning) is a set of techniques based on artificial neural networks. This underlying paradigm is revolutionizing the way applications are developed, particularly in the field of industrial vision. We’ve moved from developing deterministic models, validated by industry data and experts, to developing non-deterministic models that learn to solve problems based on a large amount of industry data and successive trials. The advantage of using these techniques is the model’s ability to learn to recognize invisible or unconscious features that a software developer could not easily incorporate into a traditional industrial vision model.
The very construction of these neural networks allows the model to adapt and recognize objects it has not been specifically trained on. This feature makes deep learning an essential tool in industrial vision for detecting various defects in a controlled production environment.
Applications in various sectors
Regardless of the industry, implementing our industrial vision software, Deep Capture, is very simple and intuitive.
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