Data Acquisition
- Does it work if I simulate changes in lighting conditions during data acquisition by manually adjusting the camera exposure or adding supplemental light?
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No. Simulated lighting conditions may not reflect the actual conditions accurately, and thus image data acquired under such conditions cannot provide accurate object features to train the model. Therefore, if the lighting conditions on-site change over the day, please acquire image data respectively under different conditions.
- The camera is fixed, and the incoming objects’ positions vary slightly. Does it work if I simulate the position changes of the objects by moving the camera during data acquisition?
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No. The camera should be fixed in position before any data collection. Moving the camera during data acquisition will affect the extrinsic parameters of the camera and the training effect. Setting a larger ROI can help fully 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?
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No. After camera replacement, all data used for model training should come from the new camera. Please conduct data acquisition again using the new camera and use the data for training.
- Will changing the background affect model performance?
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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 acquisition, it is best not to change the background afterward.
- Does it work if I use the image data acquired with different camera models at different heights together to train one model?
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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 I consider during data acquisition?
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Please avoid overexposure and underexposure. If overexposure in parts of the image is inevitable, make sure the contour of the object is clear.
- For cartons, what factors should I consider during data acquisition?
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Provide cartons in standard shapes for recognition, and avoid overexposure and underexposure. If adjusting the image brightness does not improve recognition results, please contact Mech-Mind Technical Support and provide information such as camera model, mounting height, and product details.
- How to improve unstable model performance due to complicated on-site lighting conditions, e.g., objects are covered by shadows?
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Please add shading or supplemental light as needed.