Thrips are major minute pests that threaten crop growth, and their early and accurate detection is of great significance for pest monitoring and precision control. Existing image-based thrips detection studies have mainly focused on insects captured on sticky traps, whereas direct detection of thrips on plant surfaces under complex leaf-background conditions remains relatively underexplored. Moreover, in leaf-background images, thrips are extremely small, have indistinct contours, and are susceptible to interference from noise such as leaf textures and spots, resulting in insufficient detection and counting accuracy. To address these issues, this study proposes SCIC-DEIM, a small-object thrips detection model based on the end-to-end detector DEIM. First, leaf-background thrips images were collected at Jingchu University of Technology to construct a dataset containing 5618 images and 47,726 thrips instances. Second, focusing on three key aspects—background redundancy suppression, target response enhancement, and detail information recovery—the SCIC-DEIM model was developed. Specifically, the SCConv and IIA modules were introduced into the backbone network to construct SCIStage, and CARAFE was adopted to replace conventional upsampling operations. Experimental results show that, compared with DEIM, SCIC-DEIM improves Precision, mAP@50, and mAP@50:95 by 2.4, 2.0, and 2.9 percentage points, respectively, while reducing GFLOPs by approximately 20.6%. It also achieves improved MAE, RMSE, and R² values compared with DEIM. The proposed algorithm demonstrates strong capability in small-object thrips detection under leaf-background conditions and can provide a methodological reference for intelligent monitoring of tiny pests on leaf surfaces in protected agriculture.