Accurate body-weight measurement is important for precision pig farming, but conventional weighing methods are labor-intensive and may disturb normal animal activity. Although three-dimensional sensing systems can provide reliable geometric information, their deployment cost limits large-scale application in commercial farms. This study proposes a non-contact pig weight estimation framework based on standard RGB images. The framework combines SAM 2 foreground extraction with a transformer-based dorsal segmentation network to obtain stable body contours under complex farm conditions. Cross-covariance attention and local patch interaction modules are introduced to preserve both global body structure and local boundary details during segmentation. A hybrid loss function combining focal loss and label-distribution-aware margin loss is further adopted to address foreground-background imbalance. After segmentation, 17 morphometric features are extracted from the dorsal region and used for weight prediction with XGBoost regression. Experiments were conducted on the public PIGRGB-Weight dataset containing 12,476 RGB images from 124 pigs. The proposed method achieved a mean absolute error of 2.983 kg and an $R^2$ value of 0.9891. Compared with a DeepLabV3+-based baseline under the same regression protocol, the proposed framework reduced the prediction error by 24.1%. The results indicate that improving dorsal segmentation quality can substantially enhance the stability of morphometric feature extraction from low-cost RGB images.

High-resolution remote sensing image change detection holds significant application value in the fields of urban planning, disaster assessment, and others. However, it faces the dual challenge of pseudo-change interference and loss of detailed information. To address these issues, a frequency-domain-aware Siamese detail recovery network (FSD-Net) is designed in this paper. Firstly, from the perspective of frequency domain analysis, a theory on the dual roles of frequency domain components is introduced to reveal the robustness of low-frequency components to pseudo-changes and the dual semantic noise attributes of high-frequency components. Based on this theory, a frequency-aware context-guided difference (FCGD) module is designed. By explicitly decoupling the difference features into low-frequency global components and high-frequency residual components, it utilizes the prior low-frequency scene as a semantic gate to adaptively modulate the high-frequency differences, which effectively suppress pseudo-change interference. Subsequently, a detail recovery block (DRB), based on sub-pixel convolution, is constructed. This achieves unbiased spatial rearrangement through the semantic redundancy of channel dimensions, which avoids the checkerboard artifacts of traditional upsampling, and by employing a progressive multi-stage upsampling strategy to integrate shallow detail features from the encoder. The experimental results on the three public datasets of LEVIR-CD, WHU-CD, and CDD-CD demonstrate that the FSD-Net outperforms current mainstream methods (e.g., ChangeFormer, BAN, and so on) in core metrics such as F1 score and IoU, with a particularly significant improvement in recall. The ablation experiments validate the effectiveness and complementarity of the FCGD and DRB. Parameter sensitivity analysis indicates that the auxiliary loss weight λ is dataset dependent, with λ = 0.1 serving as a robust default choice. This study provides an efficient and reliable solution for change detection in high-resolution remote sensing imagery.