YOLOV8 detection based on image enhancement

[jJIANGHE5416]

I'm a beginner to object detection tasks, how can I combine low-light image enhancement with the YOLOV8 network without sacrificing accuracy?

[glenn-jocher]

@jJIANGHE5416 hello! Combining low-light image enhancement with YOLOv8 can indeed help with detection in challenging lighting conditions. A common approach is to preprocess your images for enhancement before feeding them into the YOLOv8 model for detection. Here's a basic outline you can follow using Python:

from ultralytics import YOLO
import cv2

# Load your YOLOv8 model
model = YOLO('yolov8n.pt')

# Preprocess your image
image_path = 'path/to/your/low-light/image.jpg'
img = cv2.imread(image_path)

# Apply your low-light enhancement algorithm (example with simple histogram equalization)
img_enhanced = cv2.equalizeHist(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY))

# Detect objects
results = model(img_enhanced)

# Process results...
results.show()

Remember, the effectiveness of this method heavily depends on the enhancement technique used. Experiment with different enhancement methods to see what works best for your specific dataset! 🌟

[jJIANGHE5416]

Thank you for your help in improving my low-light detection task. I want to know if the way to integrate enhancement technology into YOLOV8 backbone is correct. For example:

Ultralytics YOLO 🚀, AGPL-3.0 license

YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect

Parameters

nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'

[depth, width, max_channels]

n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs

YOLOv8.0n backbone

backbone:

[from, repeats, module, args]

• [-1, 1, CPA_arch, []] # 0-P1/2
• [-1, 1, Conv, [64, 3, 2]] # 1-P1/2
• [-1, 1, Conv, [128, 3, 2]] # 2-P2/4
• [-1, 3, C2f, [128, True]]
• [-1, 1, Conv, [256, 3, 2]] # 4-P3/8
• [-1, 6, C2f, [256, True]]
• [-1, 1, Conv, [512, 3, 2]] # 6-P4/16
• [-1, 6, C2f, [512, True]]
• [-1, 1, Conv, [1024, 3, 2]] # 8-P5/32
• [-1, 3, C2f, [1024, True]]
• [-1, 1, SPPF, [1024, 5]] # 10

YOLOv8.0n head

head:

• [-1, 1, nn.Upsample, [None, 2, 'nearest']]

• [[-1, 7], 1, Concat, [1]] # cat backbone P4

• [-1, 3, C2f, [512]] # 13

• [-1, 1, nn.Upsample, [None, 2, 'nearest']]

• [[-1, 5], 1, Concat, [1]] # cat backbone P3

• [-1, 3, C2f, [256]] # 16 (P3/8-small)

• [-1, 1, Conv, [256, 3, 2]]

• [[-1, 13], 1, Concat, [1]] # cat head P4

• [-1, 3, C2f, [512]] # 19 (P4/16-medium)

• [-1, 1, Conv, [512, 3, 2]]

• [[-1, 10], 1, Concat, [1]] # cat head P5

• [-1, 3, C2f, [1024]] # 22 (P5/32-large)

• [[16, 19, 22], 1, Detect, [nc]] # Detect(P3, P4, P5)

[glenn-jocher]

@jJIANGHE5416 hello! Integrating enhancement technology directly into the YOLOv8 backbone as a preprocessing layer could potentially work, but it's not a common approach since it involves modifying the model's architecture and retraining the network. Typically, image enhancement is applied before the detection pipeline, as a separate preprocessing step. This allows you to use enhancement techniques without altering the model's internals.

If you're looking to improve low-light performance directly within the model, consider data augmentation strategies during training that simulate low-light conditions or fine-tuning your model on a low-light dataset.

For direct integration, you might experiment by inserting enhancement layers before the first convolutional layer of the backbone, but note that this requires careful consideration of the enhancement technique's compatibility with the model's architecture and extensive retraining.

Remember, the most straightforward and adaptable method would be preprocessing your images for enhancement before feeding them into YOLOv8 for detection. Keep exploring and experimenting! 🌟

[jJIANGHE5416]

Thank you very much, this is very helpful for my later work

[glenn-jocher]

@jJIANGHE5416 I'm thrilled to hear you found the information useful! 😊 If you have any more questions or need further assistance, feel free to reach out. Happy coding! 🚀

[maydo82]

@jJIANGHE5416 hello! Integrating enhancement technology directly into the YOLOv8 backbone as a preprocessing layer could potentially work, but it's not a common approach since it involves modifying the model's architecture and retraining the network. Typically, image enhancement is applied before the detection pipeline, as a separate preprocessing step. This allows you to use enhancement techniques without altering the model's internals.

If you're looking to improve low-light performance directly within the model, consider data augmentation strategies during training that simulate low-light conditions or fine-tuning your model on a low-light dataset.

For direct integration, you might experiment by inserting enhancement layers before the first convolutional layer of the backbone, but note that this requires careful consideration of the enhancement technique's compatibility with the model's architecture and extensive retraining.

Remember, the most straightforward and adaptable method would be preprocessing your images for enhancement before feeding them into YOLOv8 for detection. Keep exploring and experimenting! 🌟

In this way, there seems to be two different networks here. To train both tasks in a single network, I guess we should have a common loss function and we somehow need to integrate the enhancement module to detection backbone. I also try to understand this multitask learning for image enhancement and object detection. Am I wrong here?