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In industrial automation, threaded mechanical components present significant challenges for inspection due to their complex geometries and the high concealment of micro-defects. This paper proposes an integrated detection system for internal and external thread defects, combining image enhancement, data synthesis, lightweight detection, and adversarial defense. A unified image acquisition platform with fisheye lenses and high-definition industrial cameras enables synchronized imaging of both internal and external threads. By incorporating MLWNet-based dynamic deblurring and DarkIR-based low-light enhancement, image quality is significantly improved (PSNR 30.3 dB, SSIM 0.945). A Residual Diffusion Denoising Model (RDDM) is used to diversify samples, reducing the FID from 69.6 to 24.92. For detection, a lightweight enhanced architecture, SLF-YOLO, achieves a precision of 0.881 and mAP@0.5 of 0.813, outperforming multiple YOLO baselines. A dual defense mechanism—input perturbation suppression and output anomaly analysis—effectively mitigates over 95% of mAP loss under Alpha channel attacks. Experimental results demonstrate that the proposed system delivers robust, secure, and efficient performance, offering a practical pathway toward reliable, interpretable, and resilient industrial vision inspection.