Aiming at the problems of low detection accuracy of small targets in the quality inspection of bamboo chopsticks and leakage of detection when multiple targets are gathered and overlapped, this paper proposes an efficient bamboo chopstick slice detection method based on an improved real-time detection Transformer (RT-DETR), named EBCS-DETR. SDSEM module is designed to enhance the expressive capability of shallow features in ResNet18, improving the representation of fine-grained details. Second, FasterEMA module is introduced to reduce the number of parameters in deep layers and accelerate feature extraction in the backbone network. Third, the neck network is enhanced by integrating CARAFE module with Bi-FPN, which improves localization accuracy for small defective regions, strengthens discrimination at object boundaries and suppresses background noise. Experimental results on a self-constructed dataset of 5 400 images demonstrate that the proposed EBCS-DETR achieves mean average precision (mAP50) improvements of 3.3%, 3.4%, and 3.1% over the baseline model across three test sets, consistently outperforming other state-of-the-art models with comparable parameter counts. In addition, robust experiments further confirmed the reliability and application potential of EBCS-DETR for detecting defects in bamboo chopsticks slices, which is crucial for complex production environments. This work provides a novel and effective solution for high-precision automated inspection of bamboo chopsticks, offering valuable insights for intelligent quality assurance in similar manufacturing scenarios.