Liu, Z.; Wang, K.; Li, C.; Wang, Y.; Luo, G. TIG-DETR: Enhancing Texture Preservation and Information Interaction for Target Detection. Appl. Sci.2023, 13, 8037.
Liu, Z.; Wang, K.; Li, C.; Wang, Y.; Luo, G. TIG-DETR: Enhancing Texture Preservation and Information Interaction for Target Detection. Appl. Sci. 2023, 13, 8037.
Liu, Z.; Wang, K.; Li, C.; Wang, Y.; Luo, G. TIG-DETR: Enhancing Texture Preservation and Information Interaction for Target Detection. Appl. Sci.2023, 13, 8037.
Liu, Z.; Wang, K.; Li, C.; Wang, Y.; Luo, G. TIG-DETR: Enhancing Texture Preservation and Information Interaction for Target Detection. Appl. Sci. 2023, 13, 8037.
Abstract
In practical applications, the detection of objects with various sizes is a common requirement for most detectors. The feature pyramid network (FPN) is widely adopted as a framework to address this challenge. The field is witnessing an increasing number of transformer-based target detectors due to the widespread adoption of transformer technology. This paper initially examines the design flaws in FPN and transformer-based target detectors, followed by the introduction of a new transformer-based approach called Texturized Instance Guidance (TIG-DETR) to address these issues. Specifically, TIG-DETR comprises a backbone network, a new pyramidal structure known as Texture-Enhanced FPN (TE-FPN), and an enhanced DETR detector.The TE-FPN is composed of three components: a bottom-up pathway for enhancing texture information in the feature map, a lightweight attention module to address confounding effects resulting from cross-scale fusion, and a standard attention module to enhance the final output features.The improved DETR detector utilizes Shifted Window based Self-Attention to replace the multi-headed self-attention module in DETR, thereby accelerating model convergence. Moreover, it incorporates an Instance Based Advanced Guidance Module to enhance instance perception in the image by employing a pre-local self-attentive mechanism for recognizing larger instances. By employing TE-FPN instead of FPN in Faster RCNN with Resnet-50 as the backbone network, we achieve a 1.9% improvement in average accuracy. TIG-DETR achieves an average accuracy of 44.1 with Resnet-50 as the backbone network.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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