YOLOv5 Object Detection on Rainy Scene This project performs object detection on a rainy scene image using the YOLOv5 model. Prerequisites

Python 3.x Google Colab environment Google Drive access

Installation

Install required packages:!pip install yolov5 !pip install pillow opencv-python

Mount Google Drive:from google.colab import drive drive.mount('/content/drive')

Usage

Place the input image in your Google Drive (e.g., /content/drive/MyDrive/Colab Notebooks/Datasets/Yolo weights , classes and input image/A rainy scene .webp). Run the script to: Load the YOLOv5x model from PyTorch Hub. Read and preprocess the input image. Perform object detection. Save the output image with bounding boxes to /content/drive/MyDrive/Colab Notebooks/Datasets/Yolo weights , classes and input image/rainyoutput4.jpg.

Code import torch import cv2 import numpy as np

Load YOLOv5 model

model = torch.hub.load('ultralytics/yolov5', 'yolov5x')

Load and preprocess image

image_path = '/content/drive/MyDrive/Colab Notebooks/Datasets/Yolo weights , classes and input image/A rainy scene .webp' image = cv2.imread(image_path) image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

Perform object detection

results = model(image_rgb)

Render results

result_image = results.render()[0]

Save output

output_image_path = '/content/drive/MyDrive/Colab Notebooks/Datasets/Yolo weights , classes and input image/rainyoutput4.jpg' cv2.imwrite(output_image_path, result_image)

Output The output image (rainyoutput4.jpg) contains the input image with detected objects highlighted by bounding boxes and labels. Notes

Ensure the input image path is correct. The YOLOv5x model is used for high accuracy; other variants (e.g., yolov5s) can be used for faster inference. Internet connection is required to download the model from PyTorch Hub.