SG-RTDETR: Saliency-Guided Selective Representational Allocation for Real-Time Multi-Category Processing-Tomato Sorting

Processing tomato harvesters require real-time sorting of ripe tomatoes, unripe tomatoes, defective tomatoes, and soil clods in dense material flows. The key challenge is that category evidence is subtle, local, and spatially uneven: defect textures, fruit boundaries, and soil-clod cues are easily diluted by adjacent objects and background clutter. To address this, we propose SG-RTDETR, which adapts RT-DETRv2 to selectively allocate representation capacity to informative target regions while retaining local evidence. The model preserves high-resolution details during downsampling, applies saliency-guided token selection to focus global encoding on target-related locations, and reintegrates encoded tokens through residual spatial refill to maintain feature continuity. Context-aware feature organization and spatially adaptive cross-scale fusion further reduce fragmented responses and background-dominated fusion. Together, these operations target two main error sources: lost local cues and cluttered cross-region interactions. We constructed a four-category processing tomato dataset with 1,000 multi-object images and 400 single-object images for supplementary representation learning. Under a unified evaluation protocol, SG-RTDETR achieved 87.9% mAP50:95, 92.5% mAP50, and 95.6% mAR50:95, outperforming RT-DETRv2 by 3.4, 3.6, and 0.6 percentage points, respectively, while maintaining 103.6 FPS. These results show that selective modeling of spatially uneven evidence improves real-time tomato sorting.