Key Laboratory of Signal Detection and Processing, Department of Information Science and Engineering, Xinjiang University, Urumqi 830017, China.
Sensors (Basel). 2023 Jun 21;23(13):5789. doi: 10.3390/s23135789.
Camouflaged object detection (COD) aims to segment those camouflaged objects that blend perfectly into their surroundings. Due to the low boundary contrast between camouflaged objects and their surroundings, their detection poses a significant challenge. Despite the numerous excellent camouflaged object detection methods developed in recent years, issues such as boundary refinement and multi-level feature extraction and fusion still need further exploration. In this paper, we propose a novel multi-level feature integration network (MFNet) for camouflaged object detection. Firstly, we design an edge guidance module (EGM) to improve the COD performance by providing additional boundary semantic information by combining high-level semantic information and low-level spatial details to model the edges of camouflaged objects. Additionally, we propose a multi-level feature integration module (MFIM), which leverages the fine local information of low-level features and the rich global information of high-level features in adjacent three-level features to provide a supplementary feature representation for the current-level features, effectively integrating the full context semantic information. Finally, we propose a context aggregation refinement module (CARM) to efficiently aggregate and refine the cross-level features to obtain clear prediction maps. Our extensive experiments on three benchmark datasets show that the MFNet model is an effective COD model and outperforms other state-of-the-art models in all four evaluation metrics (Sα, Eϕ, Fβw, and MAE).
伪装目标检测（COD）旨在分割那些与周围环境完美融合的伪装目标。由于伪装目标与其周围环境之间的边界对比度较低，因此它们的检测具有很大的挑战性。尽管近年来已经开发出许多优秀的伪装目标检测方法，但边界细化和多层次特征提取和融合等问题仍需要进一步探索。本文提出了一种用于伪装目标检测的新型多层次特征融合网络（MFNet）。首先，我们设计了一个边缘引导模块（EGM），通过结合高层语义信息和底层空间细节，提供额外的边界语义信息，来改善 COD 性能，从而对伪装目标的边缘进行建模。此外，我们提出了一个多层次特征融合模块（MFIM），它利用低层次特征的精细局部信息和相邻三个层次特征的高层次特征的丰富全局信息，为当前层次特征提供补充特征表示，有效地融合了完整的上下文语义信息。最后，我们提出了一个上下文聚合细化模块（CARM），用于有效地聚合和细化跨层特征，以获得清晰的预测图。我们在三个基准数据集上进行了广泛的实验，结果表明，MFNet 模型是一种有效的 COD 模型，在所有四个评估指标（Sα、Eϕ、Fβw 和 MAE）上均优于其他最先进的模型。
