Object Detection System on Raspberry Pi

本科毕业设计，一个部署到树莓派上的轻量化目标检测系统平台。

Overview

This project provides an object detection system running on a Raspberry Pi.

It supports multiple input modes (image, video, and camera) and allows users to select different models for detection.

The system also exports detection performance (FPS) results into an Excel file for further analysis.

System Workflow

1. Run main.py on the Raspberry Pi environment.
2. Launch the Object Detection System Platform.
3. Select the detection model and input source.
4. Perform object detection on the chosen input (image, video, or camera).
5. Export detection results (FPS) to an Excel file in the project directory.

Usage Guide

1. Run on Raspberry Pi：Open the terminal and navigate to the project directory

   cd /home/pi/your_project

   Run the main program:

   python3 main.py

2. Features of the Object Detection Platform

   • Main Interface：Provides access to model selection and input source configuration.

     

   • Model Selection：

     • Choose between available object detection models (e.g., YOLO series or other integrated models).
     • The system automatically loads the corresponding weights.
     

   • Image Input：

     • Upload single image.

       

     • The system performs object detection and displays the results.

       

   • Video Input：

     • Select a local video file.

       

     • The system processes the video frame by frame with detection results shown in real time.

       

   • Camera Input：

     • Supports Raspberry Pi’s built-in camera or external USB cameras.

     • Enables real-time object detection from the live camera stream.

       

   • Result Export：

     • The system records the detection FPS during runtime.

     • After detection, the FPS results are automatically exported into an Excel file.

     • The file is stored in the project directory for later analysis.