Animal Detection Train CNN Flask in Python Projects
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₹800.00₹4,000.00 -
₹1,800.00
₹4,500.00 -
₹800.00
₹4,000.00 -
₹1,800.00
₹4,500.00 -
₹800.00
₹4,000.00
Animal Detection Train CNN Flask in Python Projects
Abstract
Animal detection is an important computer vision task with applications in wildlife monitoring, smart farming, and safety systems. Traditional detection methods depend on manual observation, which is time-consuming and often inaccurate in large-scale environments. This project, Animal Detection using CNN and Flask in Python, focuses on training a Convolutional Neural Network (CNN) for identifying animals from images or video frames and deploying the trained model through a Flask web application. The CNN automatically extracts features from input images and classifies them into different animal categories. Python libraries such as TensorFlow/Keras, OpenCV, and NumPy are used for model training and preprocessing, while Flask provides an interactive platform for users to upload images and receive real-time detection results. This system is designed to assist in automated animal recognition, enabling fast and efficient decision-making in practical applications.
Existing System
Currently, animal detection in most cases relies on manual observation or sensor-based methods such as motion detectors and RFID tags. While these techniques provide basic monitoring, they lack accuracy and fail to classify specific animals. Some computer vision–based systems use traditional image processing methods like edge detection, color histograms, or texture analysis, but these are limited in handling variations in lighting, pose, or background complexity. Existing systems also often require high-end equipment and do not provide real-time usability or accessible web interfaces, making them less practical for general users, farmers, or researchers.
Proposed System
The proposed system introduces a deep learning–based animal detection framework built with CNNs for accurate image classification. The CNN is trained on an animal dataset (e.g., dogs, cats, cows, lions, etc.) to automatically learn robust features. Once trained, the model is integrated into a Flask application, where users can upload images through a web interface. The backend processes the image, runs it through the trained CNN model, and displays the predicted animal type along with confidence scores. This system improves upon existing methods by providing automation, real-time usability, higher accuracy, and a user-friendly web platform. It can be further extended for wildlife monitoring, livestock management, and intelligent surveillance applications.
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