Small object change detection (SOCD) based on high-spatial resolution (HSR) images is of significant practical value in applications such as the investigation of illegal urban construction, but little research is currently available. This study proposes an SOCD model called TMACNet based on a multitask network architecture. The model modifies the YOLOv8 network into a Siamese network and adds structures, including a feature difference branch (FDB), temporal mutual attention layer (TMAL) and contextual attention module (CAM), to merge differential and contextual features from different phases for the accurate extraction and analysis of small objects and their changes. To verify the proposed method, an SOCD dataset called YZDS is created based on unmanned aerial vehicle (UAV) images of small-scale solar water heaters on rooftops. The experimental results show that TMACNet exhibits strong resistance to image registration errors and building height displacement and prevents error propagation from object detection to change detection originating from overlay-based change detection. TMACNet also provides an enhanced approach to small object detection from the perspective of multitemporal information fusion. In the change detection task, TMACNet exhibits notable F1 improvements exceeding 5.96% in comparison with alternative change detection methods. In the object detection task, TMACNet outperforms the single-temporal object detection models, increasing accuracy with an approximately 1–3% improvement in the AP metric while simplifying the technical process.

中文翻译：

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通过孪生网络在无人机图像中进行小物体变化检测，增强时间相互关注和上下文特征：关于太阳能热水器的案例研究


基于高空间分辨率 （HSR） 图像的小目标变化检测 （SOCD） 在城市违法建设调查等应用中具有重要的实用价值，但目前研究较少。本研究提出了一种基于多任务网络架构的名为 TMACNet 的 SOCD 模型。该模型将 YOLOv8 网络修改为孪生网络，并增加了结构，包括特征差异分支 （FDB） 、时间互注意力层 （TMAL） 和上下文注意力模块 （CAM），以合并来自不同阶段的差分和上下文特征，以准确提取和分析小目标及其变化。为了验证所提出的方法，基于屋顶小型太阳能热水器的无人机 （UAV） 图像创建了一个名为 YZDS 的 SOCD 数据集。实验结果表明，TMACNet 对图像配准误差和建筑物高度位移表现出很强的抵抗力，并防止了来自基于叠加的变化检测的误差从目标检测到变化检测的传播。TMACNet 还从多时态信息融合的角度提供了一种增强的小目标检测方法。在变化检测任务中，与其他变化检测方法相比，TMACNet 表现出显着的 F1 改进超过 5.96%。在对象检测任务中，TMACNet 的性能优于单时态对象检测模型，提高了准确性，AP 指标提高了约 1-3%，同时简化了技术流程。