Query Regarding Extension of Vehicle Detection Capabilities for Varied Weather Conditions

[yihong1120]

Dear Ahmet Özlü,

I hope this message finds you well. I have been exploring your remarkable project on vehicle detection, tracking, and counting using the TensorFlow Object Counting API, and I must commend the impressive work you have accomplished thus far.

As I delved into the capabilities of your sample project, I noticed that the detection and classification of vehicles are robust under standard weather conditions. However, I am curious about the system's performance in diverse weather scenarios, such as heavy rain, fog, or snow, which are quite common in the UK.

Given that adverse weather conditions can significantly impact visibility and the accuracy of vehicle detection, I was wondering if there are any plans to enhance the model to cope with such environmental factors. The ability to maintain high accuracy in poor weather conditions would be invaluable for real-world applications, particularly in regions with unpredictable weather patterns.

Additionally, I would be interested to know if there are any recommended approaches or modifications that could be made to the existing system to improve its resilience against such challenges. Your insights or suggestions on this matter would be greatly appreciated.

Thank you for your time and consideration. I look forward to your response and any guidance you can provide.

Best regards,
yihong1120