TOD-Net: An end-to-end transformer-based object detection network

Abstract

Various object detection approaches using a learning model intends to learn the semantic and multi-scaling information to attain superior object saliency. This research employs a transformer-based network framework for object detection (TOD − Net) for object detection. It is composed of encoders, decoders, and transformer and predictor module. The predictor model bridges the connectivity between the encoder and the transformer module and offers better insight into the transformer module's local measures. Here, feature extraction is performed to measure the local features and establishes dense modeling by analyzing local features. The model gives broader knowledge of local and global features. Python programming was used to experiment with the MS COCO dataset (Microsoft Common Objects in Context) where the experimentation gives better results over existing models. In contrast to existing methods, the proposed method achieves 68.7% precision and 4% accuracy. The proposed model outperforms different prevailing approaches and establishes a better trade-off.