BERT-Based Deep Learning Models for Enhanced Object Detection in Images

This article is dedicated to a latest method powered by BERT (Bidirectional Encoder Representations from Transformers) for boosting objects detection with images. Our proposed method yields important outcomes as tested on COCO dataset (Common Objects in Context), standing out with mAP 0.85. Particularly though, the trained model performs wonderfully by being able to detect small objects with an mAP of 0.78 for objects less than 32x32 pixels and by localizing partially occluded objects with an mAP of 0.82. Integrating BERT's context understanding along with the object detection frameworks, the above-said methodology delivers improved accuracy and better context-based detection as it can better understand the actual words. Ethical implication of the model in real-time inference experiments shows that the model is effective, considering an average of 30 frames per second on a standard GPU. The performance of the mentioned method is a testimony to its broad applications in the real world, such as driverless vehicles, surveillance systems, and medical imaging presenting a potential to be even more effective and efficient compared to current alternatives especially when dealing with more complicated objects and situations.