Small-object detection in large-field-of-view （FOV） surveillance videos remains a persistent challenge in real-world applications such as construction-site safety monitoring，traffic supervision in highway service areas，and public-space management on university campuses. Compared with conventional close-range datasets，targets in large-FOV imagery are often extremely small and are heavily affected by background clutter，illumination variation，compression noise，motion blur，and frequent occlusion. Therefore，detectors trained on generic benchmarks typically face three intertwined bottlenecks： 1） scarce and costly annotations for tiny objects across diverse surveillance scenes； 2） weak and ambiguous visual features caused by limited pixel coverage and low signal-to-noise ratio，which makes tiny targets difficult to distinguish from the background； and 3） inaccurate localization，where even a deviation of a few pixels can substantially reduce intersection over union （IoU），thereby harming high-precision metrics and downstream tracking or analysis. To address these issues in a unified manner，this study proposes SOD-YOLO，a small-object-oriented detector designed for low-resolution，large-FOV surveillance videos. The primary goal is to enhance supervision signals，strengthen tiny-feature representation，and improve high-precision localization simultaneously，thereby delivering reliable performance under practical surveillance constraints.

SOD-YOLO follows a data-feature-localization co-design philosophy and introduces improvements in three aspects. 1） Virtual-real fusion sample generation. To alleviate the scarcity of real annotations and increase the diversity of tiny-object appearances and backgrounds，we propose a cost-effective data-generation strategy that combines SAM2 （segment anything model v2） semantic guidance with unreal engine （UE） virtual simulation. SAM2 is used to obtain fine-grained object masks and semantic cues，enabling high-quality object cutout，composition，and accurate label transfer for small objects. UE is used to generate controllable virtual surveillance scenes with precise ground-truth annotations，in which camera viewpoints，object-scale distributions，lighting conditions，and scene layouts are configured to mimic real surveillance patterns. These synthetic data are mixed with real samples to expand the training distribution，improving robustness to background variation and rare target configurations while keeping labeling costs manageable. 2） Tiny-object feature enhancement. Because tiny targets provide weak cues that are easily overwhelmed in large-FOV frames，we design a feature-enhancement mechanism with three components. First，video differential preprocessing explicitly highlights subtle foreground changes and suppresses irrelevant static textures. Second，strengthened multi-scale feature fusion better integrates fine-grained spatial detail from shallow layers with semantic context from deeper layers，allowing the network to preserve small-object signals while retaining discriminative context. Third，bilevel routing attention （BRA） is incorporated to allocate attention adaptively to informative regions and improve the model’s ability to perceive and distinguish tiny targets under clutter and occlusion. 3） Localization-oriented optimization. To address the strong localization sensitivity of small objects，SOD-YOLO adopts a decoupled head that separates classification and regression learning，reducing task conflict and stabilizing optimization when supervision is weak. In addition，we propose an α-CIoU loss to improve regression precision and encourage better alignment at higher IoU thresholds，directly targeting the common failure mode of loose or shifted bounding boxes for tiny objects. Collectively，these designs form an end-to-end detector that improves learning from limited data，enhances tiny-feature representation，and refines bounding-box regression.

Extensive experiments were conducted on three real-scene large-FOV surveillance datasets： construction site，highway service area，and university campus，covering multi-category detection of pedestrians，e-bikes，cars，trucks，and buses，as well as extremely small targets under complex backgrounds. Evaluation followed COCO-style metrics，with particular emphasis on high-IoU and small-object performance. On the construction-site dataset，SOD-YOLO achieved the best localization-sensitive performance，with an AP75 of 13.5% and an AP50s of 53.9%，indicating superior high-precision bounding-box regression and sensitivity to small targets in cluttered scenes. On the highway service-area dataset，which is characterized by dense traffic and large intra-class variation，SOD-YOLO reached the best overall AP of 42.6% and an AP75 of 29.5%，demonstrating robust general performance and high-IoU accuracy under complex multi-class conditions. On the especially challenging university-campus dataset，where the smallest objects may occupy only about 0.007 5% of image pixels and minor offsets severely degrade IoU，SOD-YOLO clearly outperformed the YOLOv7 baseline，improving AP，AP75，and AP50s by approximately 4.9%，2.5%，and 5.0%，respectively. To further verify generalization，we evaluated SOD-YOLO on the public VT-TOD visible benchmark under the official split. The model achieved leading performance，with a precision of 61.4%，an AP50：95 of 52.3%，an APs of 63.5%，and an APm of 58.2%，outperforming representative methods such as QueryDet，YOLOC，and Drone-YOLO. Ablation studies confirmed that every proposed component contributes positively： differential preprocessing produced the most notable gain by enhancing foreground saliency； multi-scale fusion and BRA provided additional gains by strengthening discriminative representations； and the decoupled head together with α-CIoU consistently improved high-IoU localization quality. Experiments on virtual-real mixing ratios showed that a balanced fusion strategy best exploits synthetic diversity while preserving realism，whereas overreliance on synthetic data can reduce generalization because of domain gaps.

This study proposes SOD-YOLO as an effective and reliable solution for small-object detection in low-resolution，large-FOV surveillance videos. By integrating virtual-real fusion data generation，tiny-feature enhancement，and localization-oriented optimization，SOD-YOLO improves both robustness and precision across diverse real surveillance scenarios and a public benchmark，particularly under high-IoU and small-object settings. The proposed framework provides a practical pathway toward deploying accurate small-object detectors in real monitoring systems where annotation scarcity，weak features，and localization sensitivity are critical constraints.