Reliable test-tube detection on clinical conveyor lines remains difficult when tubes are densely packed, placed irregularly, weakly illuminated, partially blurred by robot vibration, and contaminated by glare from glass or PET surfaces. These conditions erode the short-axis boundary cues and faint graduation marks that slender tubes depend on. We therefore build WDA-TNET on YOLOv11 and target the failure modes at four points of the pipeline. First, WGSR restores blurred regions selectively based on wavelet energy, avoiding over-sharpening specular areas. Second, GSCIM suppresses glare-dominated channel responses in the backbone through direction-aware pooling and cross-channel interaction, retaining weak structural cues like liquid-level edges. Third, DCPAF separates height and width encoding in the neck, dynamically balancing long-axis context and short-axis localization suitable for elongated targets. Finally, ATSS and MPDIoU stabilize supervision when positives are sparse and boxes overlap only weakly. We evaluated our model on the newly constructed Complex Test Tube (CTT) dataset containing 11,955 images and 81,044 instances. WDA-TNET achieves 94.1% precision and 79.1% mAP_50:95, improving mAP_50:95 by 3.6 percentage points over YOLOv11. On the transparent-container HeinSight4 dataset, the model attains 95.2% mAP_50:95, proving robust cross-domain generalization.

In unmanned pharmacy and home-care medicine management applications, reliable pillbox localization is a prerequisite for automated dispensing and grasping. However, existing detectors still perform poorly in complex environments where dense stacking, occlusion, weak illumination, and high inter-class similarity occur simultaneously. To address this problem, GSPM-YOLO is proposed as an improved detector built on the YOLOv11 framework for complex pillbox recognition, and four novel plug-and-play lightweight modules are developed: GSimConv, a lightweight dual-branch convolution module that incorporates the Attention Weight Calculation Algorithm in HardSAM for edge-preserving feature extraction, PSCAM for position-sensitive coordinate attention, MSAAM, a multi-scale strip-pooling module that integrates the Horizontal Context-Aware Attention weight calculation algorithm to strengthen occluded targets, and LGPFH for bidirectional ghost pyramid fusion. To simulate the complex operating environments of dispensing robots, we construct MBox-Complex, a dataset of 3{,}041 images with 8{,}153 annotations across 25 drug categories. Ablation experiments first validate the effectiveness of the four-module composition, with F1 rising from 0.641 to 0.714, and each module is then individually compared with advanced replacement schemes in dedicated substitution experiments to verify its own effectiveness. The integrated model is then benchmarked against advanced detectors and domain-specific methods on the self-constructed MBox-Complex dataset, achieving 0.727 mAP@50 and 0.427 mAP@50-95 with 3.8M parameters and surpassing YOLOv11 by 7.1 and 4.0 percentage points and YOLOv12 by 4.3 and 3.1 percentage points, respectively. Further cross-dataset evaluation on the VOC and Brain Tumor benchmark datasets verifies the transferability of the proposed model. Grad-CAM is adopted to visualize the detector's attention distribution, and the resulting heatmaps together with detection visualizations confirm that the proposed model focuses more precisely on stacked and occluded regions.