To address the low level of automation in drill string handling and make-up/break-out operations on drilling platforms, an improved multi-object detection algorithm is proposed for accurate identification of drill pipes and tool joints. The model employs a lightweight EfficientNetV2 as the backbone, with an SPPF module to reduce parameters. A CBAM attention module is integrated to suppress background interference. BiFPN replaces PANet to enhance multi-scale feature fusion, while CARAFE is adopted to improve upsampling quality and feature representation. In the detection head, GhostConv is used to reduce computational cost, and the SIoU loss is introduced to improve bounding box regression. The AdamW optimizer is applied to accelerate convergence and enhance generalization. Experiments on a self-built dataset demonstrate that the proposed method achieves robust performance under complex conditions, accurately detecting drill strings with varying poses. The detection accuracy reaches 90.6% and mAP reaches 94.6%, representing improvements of 3.9% and 4.5% over the baseline, respectively, confirming its effectiveness and robustness.