Abstract:
Object detection plays a key role in helping to enable industrial quality control and safety monitoring. This article introduces a lightweight and efficient transformer-b...Show MoreMetadata
Abstract:
Object detection plays a key role in helping to enable industrial quality control and safety monitoring. This article introduces a lightweight and efficient transformer-based object detection network called the encoder-free DEtection TRansformer (EF-DETR). This novel architecture enhances the DETR model through a redesigned network structure, leading to improved accuracy in object detection and a more lightweight network. To address the issue of suboptimal object detection accuracy, especially for small objects in the DETR model, we introduce a multiscale feature extractor and a high-efficiency feature fusion module. These components facilitate the direct extraction of fine-grained features, thereby enabling effective object detection. Departing from the use of a high-complexity encoder structure, we explore the utilization of an encoder-free neck structure to reduce the network's computational complexity. In addition, to expedite convergence, denoising training is incorporated into the decoder. This article presents extensive experiments, and the EF-DETR demonstrates strong performance on the MS COCO2017 dataset compared to other popular models.
Published in: IEEE Transactions on Industrial Informatics ( Volume: 20, Issue: 11, November 2024)