Impact of Grayscale Inference on RGB-trained YOLOv8 (QR-Like object detection): Is it better to train on Grayscale directly?

[Riccardo_Carraro]

I am working on a project to detect object similar to QR codes printed on A4 paper sheets and positioned in challenging environments (excavation sites, mud, shadows).

My current pipeline uses YOLOv8n.

• Training Data: 2.5k synthetic images (RGB, various lighting conditions).

• Test Data: Real-world images captured on-site (high resolution, diverse lighting).

The Observation:

I noticed a significant Sim2Real gap. My RGB-trained model had high Precision but low Recall (~69%) on real images due to texture/lighting differences.

However, I discovered that by applying a simple pre-processing step during inference (Grayscale conversion + CLAHE), the Recall jumped to ~89% without retraining.

Effectively, I am passing a 3-channel image where all the RGB channls are the same R=G=B to a model trained on full RGB synthetic data.

My Questions:

1. Theoretical Impact: Does passing a “fake RGB” image (3 identical grayscale channels) to a standard RGB-trained YOLO model degrade its feature extraction capabilities in theory? Or does YOLO naturally filters out color information when detecting geometric shapes, focusing mostly on luminance gradients?

2. Training Strategy: Given that my object is inherently monochromatic (black patterns on white paper) and color in the environment acts mostly as noise (mud, grass):

   • Should I align my training pipeline to convert all synthetic images to Grayscale (+CLAHE) before training?

   • Or is it better to keep training on RGB (to leverage pre-trained COCO weights better) and only use Grayscale as a domain adaptation trick during inference?

I suspect the model was overfitting on the specific “synthetic colors” of the dataset, and grayscale inference forced it to look at geometric features, acting as a domain normalization.

Any insights on RGB vs Grayscale training for geometric/monochromatic object detection would be appreciated!

[Toxite]

@Riccardo_Carraro

• Theoretical Impact: Does passing a “fake RGB” image (3 identical grayscale channels) to a standard RGB-trained YOLO model degrade its feature extraction capabilities in theory? Or does YOLO naturally filters out color information when detecting geometric shapes, focusing mostly on luminance gradients?

YOLO does what it learnt to do during training. There’s nothing fixed that it does. It all depends on what it learnt.

@Riccardo_Carraro

• Training Strategy: Given that my object is inherently monochromatic (black patterns on white paper) and color in the environment acts mostly as noise (mud, grass):
• Should I align my training pipeline to convert all synthetic images to Grayscale (+CLAHE) before training?
• Or is it better to keep training on RGB (to leverage pre-trained COCO weights better) and only use Grayscale as a domain adaptation trick during inference?

You should train the model on images that are closer to what it looks like during inference. If inference is on grayscale, you can train a single channel model. The pretrained weights would largely not be affected.

COCO8-Grayscale Dataset

[Riccardo_Carraro]

@Toxite

You should train the model on images that are closer to what it looks like during inference. If inference is on grayscale, you can train a single channel model. The pretrained weights would largely not be affected.

Thanks for the feedback. To be more specific about my use case: I am detecting geometric markers (similar to QR codes) in muddy/outdoor environments. The object itself is strictly monochromatic (black patterns on white), so color variance in the real world (mud, grass, shadows) acts primarily as noise.

I observed that preprocessing real images at inference time (while trained on rgb data) with Grayscale conversion + CLAHE (and then stacking them to 3 channels to match the expected input) boosted my Recall from ~45% to ~86% using the standard RGB pretrained weights.

I have two follow-up technical questions regarding the architecture:

1. 3-Channel Replication Strategy: Is passing a “Fake RGB” image (3 identical grayscale channels) to a standard RGB-trained model considered a valid domain adaptation strategy? Or does this theoretically hinder the model by providing redundant information across channels?

2. channel: 1 Implementation in YOLOv8: If I set channel: 1 in my .yaml file to train directly on grayscale:

   • Does YOLOv8 automatically modify the first convolutional layer structure (changing input depth from 3 to 1)?

   • How are the pretrained COCO weights (which expect 3 channels) handled? Is it ok for yolo that is pre-trained over RGB data to receive input images where the 3 RGB channels are all the same?

I’m trying to decide between sticking with the 3-channel replicated input (which works well but feels “hacky”) or moving to a native 1-channel architecture. Thanks!

[Toxite]

@Riccardo_Carraro

• 3-Channel Replication Strategy: Is passing a “Fake RGB” image (3 identical grayscale channels) to a standard RGB-trained model considered a valid domain adaptation strategy? Or does this theoretically hinder the model by providing redundant information across channels?

This is what occurs automatically if you have grayscale image and the model was trained for RGB. Ultralytics will convert it to RGB by duplicating channel. There’s no definitive way to tell whether it’s beneficial or not. Because each model is different. It seems to be beneficial in your case as you highlighted.

@Riccardo_Carraro

• channel: 1 Implementation in YOLOv8: If I set channel : 1 in my .yaml file to train directly on grayscale:
• Does YOLOv8 automatically modify the first convolutional layer structure (changing input depth from 3 to 1)?
• How are the pretrained COCO weights (which expect 3 channels) handled? Is it ok for yolo that is pre-trained over RGB data to receive input images where the 3 RGB channels are all the same?

Ultralytics would change the input channel to 1. The weights for only the input layer wouldn’t be compatible. The rest of model can load pretrained weights normally.