LAGSwin: Local attention guided Swin-transformer for thermal infrared sports object detection

Abstract

Compared with visible light images, thermal infrared images have poor resolution, low contrast, signal-to-noise ratio, blurred visual effects, and less information. Thermal infrared sports target detection methods relying on traditional convolutional networks capture the rich semantics in high-level features but blur the spatial details. The differences in physical information content and spatial distribution of high and low features are ignored, resulting in a mismatch between the region of interest and the target. To address these issues, we propose a local attention-guided Swin-transformer thermal infrared sports object detection method (LAGSwin) to encode sports objects’ spatial transformation and orientation information. On the one hand, Swin-transformer guided by local attention is adopted to enrich the semantic knowledge of low-level features by embedding local focus from high-level features and generating high-quality anchors while increasing the embedding of contextual information. On the other hand, an active rotation filter is employed to encode orientation information, resulting in orientation-sensitive and invariant features to reduce the inconsistency between classification and localization regression. A bidirectional criss-cross fusion strategy is adopted in the feature fusion stage to enable better interaction and embedding features of different resolutions. At last, the evaluation and verification of multiple open-source sports target datasets prove that the proposed LAGSwin detection framework has good robustness and generalization ability.

PHSI-RTDETR: A Lightweight Infrared Small Target Detection Algorithm Based on UAV Aerial Photography

“…Li et al [28] developed an interpretable multi-scale infrared small object detection network, IMD-Net, to enhance the accuracy of detecting and segmenting small objects against complex backgrounds. Meng et al [29] introduced a locally focused attention-based Swin-transformer technique for thermal infrared moving object detection, termed LAGSwin, which encodes the spatial transformations and directional information of moving objects to enhance interaction and feature integration at varying resolutions. Nevertheless, the computational requirements of this model may exceed the capabilities of drone devices.…”