<p>Accurate image segmentation of experimental instruments is important for intelligent analysis and automated assessment in physical laboratory scenarios. However, practical experimental images often contain background clutter, target-scale variation, and thin structures, which challenge the feature representation and multi-scale modeling capabilities of existing segmentation networks. To address these issues, we propose an Adaptive Coordinated Aggregation Res-UNet (ACA-ResUNet) for physical experimental instrument segmentation. The model incorporates an adaptive semantic filtering (ASF) module to suppress irrelevant background responses, a hierarchical feature coordination (HFC) module to improve cross-level feature fusion, and a multi-perceptual context aggregation (MPCA) module to capture contextual information at multiple scales. These components are jointly regulated through a coordinated aggregation strategy that integrates local details, hierarchical semantics, and multi-scale context during decoding. The proposed model is evaluated on three self-constructed physical experiment datasets and two public segmentation benchmarks under unified experimental protocols. Experimental results demonstrate competitive segmentation performance on physical experimental images and provide further evidence of its applicability to external segmentation domains.</p>