Snake Identification Image Segmentation System Source Code & Dataset Sharing

1. Research Background and Significance

Research Background

With the growing awareness of global biodiversity conservation, snakes, as important components of ecosystems, are receiving increasing attention for protection and study. Traditional snake identification and classification rely on manual observation and expert knowledge, which are inefficient and subject to bias. Therefore, computer‑vision‑based automated identification and segmentation systems have emerged as essential solutions.

Research Significance

This study builds an efficient snake identification image segmentation system based on an improved YOLOv8 model. By researching and applying this system, we aim to provide a new technical means for ecological monitoring and protection of snakes, promoting automation and intelligence in snake research.

2. Dataset Information Presentation

Detailed Dataset Information

• Dataset Name: Snakes CALABARZON pt. 2
• Number of Images: 9,700
• Number of Classes: 15
• Class Names:
  • mangrove cat snake
  • northern short‑headed snake
  • paradise tree snake
  • philippine blunt‑headed catsnake
  • philippine bronzeback treesnake
  • philippine cat snake
  • philippine cobra
  • philippine pitviper
  • philippine stripe‑lipped
  • philippine whip snake
  • red‑tailed green ratsnake
  • reddish rat snake
  • reinhardts lined snake
  • reticulated python
  • smooth‑scaled mountain rat snake

Dataset Introduction

The dataset is dedicated to classification and annotation of snakes in the Philippines, featuring rich diversity and high application value. It contains 15 different snake categories, covering many species in the region, providing an ideal basis for training and improving the YOLOv8‑seg model for snake recognition and image‑segmentation tasks.

3. System Function Demonstration

Supported Functions

• Detection result table display
• Manual adjustment of confidence and IoU thresholds
• Custom loading of weight file best.pt
• Real‑time camera recognition
• Image recognition
• Video recognition
• Automatic saving of recognition result files
• Export of detection results to Excel

4. YOLOv8‑seg Image Segmentation Algorithm Principle

Algorithm Overview

YOLOv8‑seg, released by Ultralytics in January 2023, is an advanced object detection and instance segmentation model that introduces several innovations on the YOLO series to achieve higher accuracy and faster processing speed.

Core Components

• Input: Receives image data and performs preprocessing.
• Backbone: Extracts rich feature information from the input image.
• Head: Maps feature maps to specific object classes and locations.

Innovations

• C2f Module: Improves the traditional C3 module by adding more skip connections and split operations.
• Decoupled Head Structure: Separates classification and regression tasks, reducing dependence on anchor boxes.
• Multiple Loss Functions: Uses BCELoss for classification loss together with DFLLoss and CIoULoss for regression loss.

5. Core Source Code Explanation

Main Modules

• TransformerEncoderLayer: Defines a single Transformer encoder layer.
• AIFI: Defines the AIFI Transformer layer.
• TransformerLayer: Utilizes linear transformations and multi‑head attention.

Functions

• Multi‑head Self‑Attention: Processes input data and further refines it through a feed‑forward network.
• Positional Embedding: Constructs 2D sinusoidal positional embeddings to enhance spatial awareness.
• Forward Pass: Implements forward propagation with both post‑norm and pre‑norm variants.