Enhancing Dog Detection in Challenging Conditions

Requirements Clarification & Assessment

1. Objective Understanding:

   • The primary goal is to enhance the robot's ability to accurately identify and differentiate between dog breeds, particularly pugs and pit bulls, under challenging conditions such as rain, fog, and partial occlusion.

2. Dataset Quality:

   • The current dataset contains mislabeled images of pugs and pit bulls, affecting the model's ability to learn accurately.
   • Assess the proportion of mislabeled data and the impact of such errors on model performance.

3. Environmental Challenges:

   • Identify the specific environmental conditions (e.g., rain, fog, distance) that degrade model performance.

4. Algorithm Constraints:

   • Determine if there are restrictions on modifying the underlying neural network architecture or if data collection is feasible.

5. Performance Metrics:

   • Define success metrics such as accuracy, precision, recall, or F1-score specific to pug and pit bull identification.

6. Resource Assessment:

   • Evaluate available resources for data augmentation, model training, and computational power.