CQ-RT-DETR: Lightweight Cotton-Field Weed Detection with Query Competition Calibration

To address storage, computational, and latency constraints in cotton-field robots and variable-rate spraying, this study proposes CQ-RT-DETR with a two-stage design of structural compression and training-time query calibration. First, the hidden dimension of RT-DETRv2 is reduced from 256 to 192 and the number of object queries from 300 to 100, forming the lightweight B_Lite baseline. Next, the Scale-Reliability-Guided Trajectory-Consistent Competition Margin (SR-TCCM) mechanism selects persistent competing queries using cross-layer IoU trajectories and applies a budget-constrained adaptive margin based on object scale and the winner query’s localization advantage. Across five random seeds on the test set, the mean AP50–95 values of RT-DETRv2, B_Lite, and CQ-RT-DETR are 0.8867, 0.8691, and 0.8892, respectively. CQ-RT-DETR improves AP50–95 by 2.01 percentage points over B_Lite and achieves accuracy comparable to the original RT-DETRv2, while reducing the parameter count and GFLOPs by 16.93% and 23.38%, respectively. In FP16 RKNN model-level forward-pass tests on an RK3576 platform, mean latency is 54.57% lower than for RT-DETRv2, and FPS increases from 6.69 to 14.7. These results show that CQ-RT-DETR recovers the compression-induced accuracy loss without increasing inference-graph complexity and provides a favorable accuracy–efficiency trade-off.