GhostNetV2-YOLO: A Lightweight Detector for Multi-View Aesthetic Object Detection in Home Environments

With the accelerated progress of computational aesthetics and digital interior design, the demand for real-time and precise detection of aesthetic objects on edge devices has become increasingly pressing in applications such as intelligent design assistance, domestic aesthetic assessment, and augmented reality-based interior staging. As a core task in digital home aesthetics governance, virtual interior furnishing, household cultural archive development, and automated aesthetic evaluation, multi-view aesthetic object detection plays an essential role. However, this task still faces substantial difficulties arising from pronounced viewpoint variation, scale inconsistency, reflective materials, intricate decorative patterns, and cluttered indoor scenes. To address these issues, this study presents GhostNetV2-YOLO, a lightweight yet robust detection framework designed for accurate localization of aesthetic objects under unconstrained multi-view acquisition settings. The task is formally defined as closed-set detection of 10 pre-selected home aesthetic decorative items, including both planar decorative pieces and three-dimensional ornamental objects, and all performance claims are bounded within the horizontal bounding box detection paradigm. The framework incorporates three complementary components tailored to the target task. First, a task-adapted GhostNetV2 backbone is employed to enable efficient multi-scale feature extraction and long-range dependency modeling, with optimization specifically oriented toward structured aesthetic objects with stable global contours under viewpoint variation. Second, an improved Attention-based Intra-scale Feature Interaction (AIFI) module is introduced, integrating compressed QKV projection, linear attention, depthwise spatial refinement, and channel gating so that reflection-induced noise and background disturbance can be effectively reduced. Third, an enhanced Distance-IoU regression loss is adopted, in which explicit edge alignment and dynamic sample weighting are incorporated to improve boundary regression accuracy for rectangular and regularly contoured aesthetic objects. These designs jointly enhance contextual representation, boundary localization, and computational efficiency. Extensive experiments on two newly constructed multi-view aesthetic object datasets (AestheticHome-12K and AestheticHome-2K) demonstrate that the proposed detector achieves 94.80 ± 0.32%/94.20 ± 0.37% mAP@0.5, 96.30 ± 0.28%/95.60 ± 0.31% precision, and 94.70 ± 0.35%/93.80 ± 0.39% recall across two datasets (reported as mean ± standard deviation of 5 independent training runs with distinct random seeds), with only 2.89 M parameters and 6.0 GFLOPs. Statistical significance is verified via paired two-tailed t-tests with Bonferroni correction (adjusted p < 0.05) for all performance comparisons against baseline models. Compared with the YOLOv11n baseline, the method improves mAP@0.5 by 1.87–2.09 percentage points and recall by 3.27–3.48 percentage points while reducing computational cost. Notably, it also achieves 79.2–80.5% mAP@0.5:0.95, outperforming the baseline by 4.7–4.9 percentage points, indicating significantly superior localization accuracy under stricter criteria. The proposed model achieves a remarkable balance between accuracy and efficiency, making it highly suitable for deployment on resource-constrained edge devices commonly used in digital design and home aesthetic monitoring systems. The results indicate that combining lightweight long-range feature extraction optimized for rigid aesthetic objects, compact attention-based feature interaction for interference suppression, and geometry-aware regression tailored for aesthetic targets provides an effective and efficient solution for robust aesthetic object detection in real-world computational aesthetics and digital interior design applications.