FAQ¶
Is it feasible to simulate changes in lighting conditions during data collection by manually adjusting the camera exposure or adding supplemental light?
No. Simulated lighting conditions may not reflect the actual conditions accurately, and thus image data collected under such conditions cannot provide accurate object features to train the model. Therefore, if the lighting conditions on site change over the day, please collect image data respectively under different conditions.
In the actual application, the camera is fixed, and the incoming objects’ positions vary slightly. Is it feasible to simulate the position changes of the objects by moving the camera during data collection?
No. The camera should be fixed in position before any data collection. Moving the camera during data collection will affect the extrinsic parameters of the camera and the training effect.
For the case in question, setting a larger ROI can capture the changes in object position.
If the previously used camera has unsatisfactory imaging quality and is replaced by a new camera, is it necessary to add the images taken by the old camera to the dataset?
No. After camera replacement, all data used for model training should come from the new camera. Please conduct data collection again using the new camera and use the data for training.
Will changing the background affect model performance?
Yes. Changing the background will lead to recognition errors, such as false recognition or failure to recognize a target object. Therefore, once the background is set in the early stage of data collection, it is best not to change the background afterward.
Is it possible to use the image data collected with different camera models at different heights together to train one model?
Yes, but please work on the ROI settings. Select different ROIs for images taken at different heights to reduce the differences among images.
For highly reflective metal parts, what factors should be taken into consideration during data collection?
Please avoid overexposure and underexposure. If overexposure in parts of the image is inevitable, make sure the contour of the object is clear.
If the model performs poorly, how to identify the possible reasons?
Factors to consider: quantity and quality of the training data, data diversity, on-site ROI parameters, and on-site lighting conditions.
Quantity: whether the quantity of training data is enough to make the model achieve good performance.
Quality: whether the data quality is up to standard, whether images are clear enough and are not over-/underexposed.
Data diversity: whether the data cover all the situations that may occur on-site.
ROI parameters: whether the ROI parameters for data collection are consistent with those for the actual application.
Lighting conditions: Whether the lighting conditions during the actual application change, and whether the conditions are consistent with those during data collection.
How to improve unstable model performance due to complicated on-site lighting conditions, e.g., objects are covered by shadows?
Please add shading or supplemental light as needed.
Why does the inconsistency between the ROI settings of on-site data and training data affect the confidence of instance segmentation?
The inconsistency will result in objects being out of the optimal recognition range of the model, thus affecting the confidence. Therefore, please keep the ROI settings of the on-site data and training data consistent.
What scenarios is the Super Model for boxes suitable for?
It is suitable for palletizing/depalletizing boxes of single or multiple colors and surface patterns. However, please note that this Super Model is only applicable to boxes placed in horizontal layers and are not at an angle to the ground.
How to collect data for the Super Model for boxes?
Please test the Super Model first. If it cannot segment correctly sometimes, collect about 20 images of situations where the model does not perform well. Please see Box Palletizing/Depalletizing for details.
Does the image classification model work without a GPU?
No.