AI-Powered Embedded System for Rapid Detection of Veterinary Antibiotic Residues in Food-Producing Animals.

Background: Veterinary antibiotics are widely used in food-producing animals, raising public health concerns due to drug residues and the risk of antimicrobial resistance. Rapid and reliable detection systems are critical to ensure food safety and regulatory compliance. Colloidal gold immunoassay (CGIA)-based antigen-antibody test cards are widely used in food safety for the rapid screening of veterinary antibiotic residues. However, manual interpretation of test cards remains inefficient and inconsistent. Methods: To address this, we propose a complete AI-based detection system for veterinary antibiotic residues. The system is built on the Rockchip RK3568 platform and integrates a five-megapixel OV5640 autofocus USB camera (60° field of view) with a COB LED strip (6000 K, rated 5 W/m). It enables high-throughput, automated interpretation of colloidal gold test cards and can generate structured detection reports for regulatory documentation and quality control. The core challenge lies in achieving accurate and fast inference on resource-constrained embedded devices, where traditional detection networks often struggle to balance model size and performance. To this end, we propose VetStar, a lightweight detection algorithm specifically optimized for this task. VetStar integrates StarBlock, a shallow feature extractor, and Depthwise Separable-Reparameterization Detection Head (DR-head), a compact, partially decoupled detection head that accelerates inference while preserving accuracy. Results: Despite its compact size, with only 0.04 M parameters and 0.3 GFLOPs, VetStar maintains strong performance after distillation with the Bridging Cross-task Protocol Inconsistency Knowledge Distillation (BCKD) method. For our custom Veterinary Drug Residue Rapid Test Card (VDR-RTC) dataset, it achieves an mAP50 of 97.4 and anmAP50-95of 89.5. When deployed on the RK3568 device, it delivers results in just 5.4 s-substantially faster than comparable models. Conclusions: These results highlight the system's strong potential for high-throughput, cost-effective, and rapid veterinary antibiotic residue screening, supporting food safety surveillance efforts.