Issue with False Detections in Object Detection Model

[Techydeveloper12]

Hello everyone,

I'm currently working on a garbage detection project using an object detection model. While the model performs reasonably well, I've been encountering a persistent problem with false detections (false positives). These false detections are negatively impacting the reliability of my system.

Issue Details:

Dataset: I have a well-annotated dataset with accurate bounding boxes and class labels for garbage objects.
Model: I'm using YOLOv5 (or specify the object detection model) for the task.
Training: I've trained the model using standard techniques, including data augmentation, and have experimented with different configurations.

Problem:

Despite my efforts, the model continues to produce false positives, especially in challenging scenarios, such as varying lighting conditions or cluttered backgrounds. These false positives are problematic in my application as they lead to incorrect classifications.
its detecting humans , bikes , trees , also with garbage detection also that making me in a major problem.

What I've Tried:

Adjusted confidence thresholding.
Experimented with different anchor box configurations.
Increased training data diversity.

Request for Help:

I'm seeking advice and recommendations from the community on how to effectively reduce false detections in my object detection model. If you have encountered similar issues or have insights into techniques, strategies, or best practices to improve precision and reduce false positives, I would greatly appreciate your input.

Please share your experiences, suggestions, or any resources that can help me address this issue. Your assistance will be invaluable in making my garbage detection system more reliable.

Thank you in advance for your help and expertise!

SAMPLE MODEL OUTPUT WITH ONE RIGHT AND ONE CORRECT DETECTION