Traditional manual measurement of poultry comb and wattle areas poses contact-induced stress risks, zoonotic disease transmission hazards, and substantial measurement errors. A non-contact measurement system integrating YOLO v7 with an improved U-Net architecture was proposed. Three key innovations were presented: a dual-stage detection framework utilizing YOLO v7 for head pose screening and ROI extraction, effectively eliminating offangle image interference; a novel CoT-UNet model incorporating Contextual Transformer blocks into U-Net’s encoder for dynamic-static context fusion, combined with DyC-UP module employing dynamically adjustable convolution kernels to enhance irregular edge detection; a pixelarea conversion algorithm achieving precise spatial mapping through calibration coefficients. Experimental results demonstrated significant improvements: the enhanced CoT-UNet outperformed baseline models by 4.77 percentage points (comb) and 8.75 percentage points (wattle) in IoU, along with 5.31 percentage points and 5.06 percentage points precision gains respectively. Absolute measurement errors for comb (0.62~3.50cm2) and wattle (0.10~2.93cm2) showed marked superiority over manual methods (3.58~7.27cm2). Multi-scenario validation revealed stable relative errors of 2.41%~13.62% for combs and 1.00%~29.21% for wattles across varied postures (three types), angles (two positions), and distances (two levels). This automated system enabled stress-free poultry biometric measurement, providing reliable technical support for intelligent breeding selection.