发表机构
Chongqing University of Posts and Telecommunications; Brunel University London（重庆邮电大学; 布鲁内尔伦敦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
COBICount通过分离响应生成、接受和背景抑制，实现无需目标数据训练的遥感目标计数迁移，在跨域评估中取得最低平均绝对误差。
AI 中文摘要
遥感目标计数旨在估计航拍图像中建筑物、车辆或船舶的数量。大多数有监督计数器预测密度图，其总和即为目标数量，并假设类别、尺寸和背景相似。跨区域、传感器或类别应用时，通常需要目标数据或进一步训练，这可能成本高昂或不可用。我们研究仅源域计数。计数任务的训练和模型选择使用一组共享目标类别和相似成像条件的图像，每个目标用一个点标记。目标图像和信息在模型固定之前不可用。这减少了数据准备，但使迁移更加困难。在某一源域上训练的模型可能将高密度值（称为响应）置于真实目标和重复背景结构上。道路边缘、停车网格、屋顶边界和水边界可能被计为目标，造成候选原点歧义。COBICount分离了响应生成、接受和背景抑制。候选证据（CE）生成可能的响应。候选接受（CA）保留以目标为中心的紧凑响应。偏差隔离（BI）减少与重复背景结构相关的响应。它们的输出形成最终密度图。在RSOC Building上训练并直接在DOTA Large Vehicle、Small Vehicle和Ship上评估，COBICount在比较方法中实现了目标域上的最低平均绝对误差（MAE），为174.132。对于512x512输入，它使用5.07百万参数和17.41十亿浮点运算。COBICount无需目标数据或针对每个目标的训练即可改进迁移。代码将在以下网址提供：this https URL。
英文摘要
Remote sensing object counting estimates how many buildings, vehicles, or ships appear in overhead images. Most supervised counters predict a density map, whose sum gives the object count, and assume similar categories, sizes, and backgrounds. Applying them across regions, sensors, or categories often requires target data or further training, which may be costly or unavailable. We study source-only counting. Training for the counting task and model selection use one group of images that shares an object category and similar imaging conditions, with one point marking each object. Target images and information remain unavailable until the model is fixed. This reduces data preparation but makes transfer harder. A model trained on one source may place high density values, called responses, on real objects and repeated background structures. Road edges, parking grids, roof boundaries, and water boundaries may then be counted as objects, creating candidate origin ambiguity. COBICount separates response generation, acceptance, and background suppression. Candidate Evidence (CE) generates possible responses. Candidate Acceptance (CA) keeps compact responses centered on objects. Bias Isolation (BI) reduces responses associated with repeated background structures. Their outputs form the final density map. Trained on RSOC Building and evaluated directly on DOTA Large Vehicle, Small Vehicle, and Ship, COBICount achieves the lowest mean absolute error (MAE) averaged over the target domains among the compared methods, 174.132. It uses 5.07 million parameters and 17.41 billion floating point operations for a 512x512 input. COBICount improves transfer without target data or training for each target. The code will be available at: https://github.com/yixuxi22/COBICount.
Comments19 pages, 7 figures
