Bark Rescue App:

Introduction

The "Bark Rescue App" leverages artificial intelligence to support animal rescue operations. By analyzing user-submitted images, the app predicts:

• Presence of dogs: Determines if a dog is present in the image.
• Dog's emotions: Identifies the emotional state of the dog (e.g., scared, happy), if present.

This analysis facilitates the notification of relevant animal control centers for potential rescue actions.

Tech Stack

• Front-End: HTML (JavaScript is optional for basic validation)
• Back-End: Spring Boot
• Model Inference: TorchServe (deployed on cloud platforms like Google Cloud AI Platform, Amazon SageMaker)
• Machine Learning Models:

  • EfficientNet-B0: For dog detection (classification)

  • YOLOv5: For bounding box detection (if a dog is detected)

  • MobileNetV2: For emotion recognition (if a dog is detected)

Functionalities

1. User submits image: Through a simple HTML form.
2. Preprocessing: Images are resized and normalized for analysis.
3. Dog Detection (Model 1):
   • Utilizes the EfficientNet-B0 model, served via TorchServe, to calculate the probability of a dog's presence in the image.
4. Conditional Processing:
   • If the score is >= 0.5 (indicating a dog):
     • Bounding Box (Model 2):
       • YOLOv5 predicts a bounding box around the detected dog.
       • The image is preprocessed based on the bounding box coordinates.
     • Emotion Recognition (Model 3):
       • MobileNetV2 predicts the dog's emotional state from the cropped image.
   • If unlikely a dog, it skips to the next steps.
5. Decision-Making:
   • Emotion scores, especially negative ones like scared or anxious, are analyzed.
   • Contact (Email) is made with an animal control center based on the highest negative emotion score:
     • < 0.4: Considered non-threatening, and a nearby animal control center is contacted.
     • > 0.4: Considered potentially needing help, and the nearest possible animal control center is contacted.
6. Notification:
   • An automated notification, including user location and image of the dog is sent to the selected animal control center.
7. Secure Communication:
   • Implements HTTPS and encryption for all data transfers.

Limitation:

1. Will be able to give you accurate results for single dogs.
2. If this project works perfectly, I'll update the object detection part with instance segmentation, but let's not worry about that part now 😅.