5. The Head Network: Classifying and Refining Hair Follicle Detection in Yolov7

In the world of artificial intelligence and computer vision, the head network plays a critical role in the final stages of the detection pipeline. In the case of Yolov7, an advanced deep learning model for object detection, the head network is responsible for both classifying and regressing the objects detected within an image. For hair health detection, the head network is essential for accurately identifying hair follicles and determining whether they belong to specific categories (such as healthy or thinning hair) and refining the predictions made by the earlier layers, like the backbone and Feature Pyramid Network (FPN).

In this post, we will explore the functionality of the head network in Yolov7, focusing on its role in hair follicle classification, bounding box prediction, and how it contributes to refining the model’s overall performance in hair health assessments.

What is the Head Network in Yolov7?

The head network in Yolov7 is the final component in the detection pipeline, sitting after the backbone network and Feature Pyramid Network (FPN). While the backbone and FPN are responsible for extracting features from the input image and combining them at various scales, the head network is where the model makes the crucial decisions about what those features represent. Specifically, it classifies objects (in this case, hair follicles) and refines the predictions by predicting the bounding boxes that surround each detected object.

The head network is often referred to as the classifier and regressor in object detection models. It performs two essential tasks:

1. Classification: The head network classifies each detected object (hair follicle) based on predefined categories, such as “thick hair” or “thin hair.”

2. Regression: It adjusts the predicted bounding boxes to better fit the detected object, ensuring that the box correctly encapsulates the hair follicle and its position in the image.

How the Head Network Works for Hair Follicle Detection

In the context of hair health detection, the head network plays a pivotal role in both the accurate detection of hair follicles and the classification of their health status (i.e., thickness and density). Let’s explore the process in more detail:

1. Bounding Box Prediction

One of the primary functions of the head network is to predict the bounding boxes around the detected objects. In object detection tasks, a bounding box is a rectangular box drawn around the object of interest—in this case, a hair follicle.

For hair detection, the head network takes the feature maps generated by the backbone network and the FPN and predicts the position of a bounding box that best fits each hair follicle. This process involves several key steps:

• Anchor Boxes: The head network uses anchor boxes to predict the location and size of the bounding boxes. Each anchor box is a predefined box with specific dimensions that the model uses to determine whether the detected object matches the anchor box’s size and shape. For hair follicle detection, Yolov7 uses three anchor boxes for each feature point in the feature map.

• Regression Parameters: The head network adjusts the position, width, and height of the bounding boxes by predicting regression parameters for each anchor box. These parameters determine the final position of the bounding box and ensure that it fits snugly around the detected hair follicle.

• Confidence Score: The head network also assigns a confidence score to each bounding box, which reflects how likely it is that the detected object is a hair follicle. This score helps to filter out false positives and weak predictions.

By predicting and refining these bounding boxes, the head network enables Yolov7 to accurately localize hair follicles in the image, which is crucial for subsequent health assessments.

2. Hair Follicle Classification: Thick vs. Thin Hair

Once the bounding boxes are predicted, the head network performs classification to determine the type of hair follicle it is dealing with. For hair health detection, the head network classifies hair follicles into specific categories based on hair thickness and density:

• Hair Thickness: The head network classifies hair follicles as either thick hair or thin hair, depending on the diameter of the individual hair strand. Thick hair follicles typically indicate healthy hair, while thin hair follicles may suggest hair thinning or hair loss.

• Hair Density: The head network also considers the density of the hair in a particular region. Follicles that are spaced closely together indicate a dense scalp, while follicles that are more widely spaced indicate a sparse scalp, which may be a sign of hair thinning or loss.

By classifying hair follicles based on these factors, the head network helps the model assess the overall health of the scalp, providing valuable insights into the individual’s hair condition.

3. Rotated Bounding Box for Thin Hair Detection

In addition to classifying hair follicles as either thick or thin, Yolov7's head network also uses rotated bounding boxes for detecting thin hair follicles. Thin hairs are often smaller and may be oriented at different angles compared to thicker, straighter hairs. The head network addresses this challenge by incorporating a rotated bounding box, which allows it to more accurately capture the shape and orientation of thin hair follicles.

• For thick hair, the head network typically uses a rectangular bounding box, as these hairs are usually thicker and straighter.

• For thin hair, the head network adjusts the bounding box to be rotated, better fitting the hair follicle’s orientation. This enables the model to more accurately capture and classify thin hair follicles, which may otherwise be missed by standard bounding boxes.

This additional capability allows Yolov7 to distinguish between different hair types and offer more nuanced predictions about hair health.

4. Multi-Class Classification

The head network also handles multi-class classification, where each detection is categorized not just as a hair follicle, but also into a specific health category. The categories may include:

• p1: Single hair follicle, indicating lower hair density.

• p2: Two hairs per follicle, indicating moderate density.

• p3: Multiple hairs per follicle, indicating healthy, dense hair.

These classifications provide more detailed insights into the scalp’s overall condition, allowing the model to assess whether an individual is experiencing hair thinning, moderate hair loss, or healthy hair growth.

The Head Network’s Impact on Hair Health Detection

The head network’s ability to refine bounding boxes, classify hair types, and handle rotated bounding boxes for thin hair detection significantly improves the performance of Yolov7 in hair health assessments. By classifying each hair follicle based on its type and predicting its precise location, the head network ensures that Yolov7 provides accurate and reliable results for hair density and thickness.

For businesses or professionals working in the beauty and wellness industries, this level of precision allows for more personalized recommendations and treatments. Whether an individual is looking for solutions for hair thinning, hair loss, or improving hair health, the head network’s contributions to hair follicle detection enable AI models to deliver actionable insights.

Conclusion: Refining Detection with the Head Network

In summary, the head network in Yolov7 is a powerful component that refines predictions, classifies hair follicles based on thickness and density, and adjusts bounding boxes to ensure accurate localization. It plays a vital role in the overall hair health detection process, helping AI models provide detailed insights into scalp conditions. By incorporating advanced techniques like rotated bounding boxes and multi-class classification, Yolov7’s head network enhances the model's ability to detect and assess various hair types, offering businesses a tool for delivering more precise hair health assessments and treatments.