UIU-Net: U-Net in U-Net for Infrared Small Object Detection (2212.00968v1)

Abstract: Learning-based infrared small object detection methods currently rely heavily on the classification backbone network. This tends to result in tiny object loss and feature distinguishability limitations as the network depth increases. Furthermore, small objects in infrared images are frequently emerged bright and dark, posing severe demands for obtaining precise object contrast information. For this reason, we in this paper propose a simple and effective ``U-Net in U-Net'' framework, UIU-Net for short, and detect small objects in infrared images. As the name suggests, UIU-Net embeds a tiny U-Net into a larger U-Net backbone, enabling the multi-level and multi-scale representation learning of objects. Moreover, UIU-Net can be trained from scratch, and the learned features can enhance global and local contrast information effectively. More specifically, the UIU-Net model is divided into two modules: the resolution-maintenance deep supervision (RM-DS) module and the interactive-cross attention (IC-A) module. RM-DS integrates Residual U-blocks into a deep supervision network to generate deep multi-scale resolution-maintenance features while learning global context information. Further, IC-A encodes the local context information between the low-level details and high-level semantic features. Extensive experiments conducted on two infrared single-frame image datasets, i.e., SIRST and Synthetic datasets, show the effectiveness and superiority of the proposed UIU-Net in comparison with several state-of-the-art infrared small object detection methods. The proposed UIU-Net also produces powerful generalization performance for video sequence infrared small object datasets, e.g., ATR ground/air video sequence dataset. The codes of this work are available openly at \url{https://github.com/danfenghong/IEEE_TIP_UIU-Net}.

• The paper presents a novel 'U-Net in U-Net' framework that integrates RM-DS and IC-A modules to improve the accuracy of infrared small object detection.
• It leverages residual U-blocks and cross-level attention to balance local detail preservation with global context extraction.
• Extensive experiments demonstrate that UIU-Net outperforms state-of-the-art methods on key metrics like IoU and nIoU across various challenging datasets.

Overview of UIU-Net: U-Net in U-Net for Infrared Small Object Detection

The paper presents a novel framework, UIU-Net, specifically designed for addressing the challenges inherent in the detection of small objects in infrared images. Traditional methods heavily relying on standard classification backbone networks are prone to diminishing returns as network depth increases, leading to the degradation of crucial object features. Infrared small objects typically appear with high contrast against backgrounds, often saturated in brightness or darkness, necessitating precise methodologies for effective distinction and detection.

UIU-Net introduces a unique "U-Net in U-Net" architecture, wherein a smaller U-Net is integrated within a larger U-Net structure. This configuration aims to enhance multi-level and multi-scale representation learning capacities. Central to the proposed method are two distinct modules: Resolution-Maintenance Deep Supervision (RM-DS) and Interactive-Cross Attention (IC-A). RM-DS is designed to integrate Residual U-blocks within a deep supervision framework to enable multi-scale features while preserving resolution, ultimately facilitating global context information acquisition. The IC-A module complements this by encoding interactive cross-level attention to effectively balance low-level details with high-level semantics.

Extensive experimentation conducted on datasets such as SIRST and Synthetic showcases the competitive efficacy of UIU-Net, achieving superior performance relative to state-of-the-art small object detection methods. This advantage extends to video sequence datasets, such as the ATR ground/air datasets, where UIU-Net demonstrates high adaptability and robust generalization performance, highlighting its practical relevance and potential deployment in real-world scenarios.

Numerical Results and Methodological Contributions

1. Resolution-Maintenance Deep Supervision (RM-DS): RM-DS effectively incorporates residual U-blocks within a deep supervision architecture, resolving the conflict between increasing network depth and maintaining the resolution of the features. This strategic integration facilitates rich multi-scale feature learning, promoting enhanced global context representational fidelity.
2. Interactive-Cross Attention (IC-A): The IC-A module innovatively integrates cross-level interactions to optimize local contrast and detail features. By encoding and leveraging the information interplay between low-level and high-level features through attention mechanisms, UIU-Net achieves improved distinguishability of small infrared objects amidst complex backgrounds.
3. Performance Metrics: UIU-Net demonstrates superior performance across multiple quantitative metrics, including Intersection over Union (IoU) and normalized Intersection over Union (nIoU), suggesting its improved capability for precise object segmentation and detection. Experiments confirm its robustness and applicability to varying datasets, indicating a consistent and significant performance margin over competing models.

Implications and Future Directions

Practically, UIU-Net's configuration and performance suggest potential applications in diversified infrared detection and monitoring systems, such as surveillance and rescue operations, where detecting small objects in complex scenes is critical. The dual-module approach underscores the effectiveness of combining global and local feature enhancements, signaling an avenue for future exploration in deep learning architectures tailored for infrared and other challenging imaging-modalities.

Theoretically, the integration of nested networks and cross-attention mechanisms could inspire advancements in semantic segmentation methodologies, extending beyond infrared applications to domains requiring intricate feature extraction and detailed segmentation, such as medical imaging and autonomous navigation systems.

Future research might explore this architecture's adoption in multispectral or multimodal data fusion scenarios, potentially pushing the boundary of what current systems achieve in object detection. Further exploration of diverse backbones or enhanced attention mechanisms could contribute to refining infrastructure and extending the versatility of UIU-Net in broader fields.