This paper addresses the challenge of small-object detection in traffic surveillance by proposing a hybrid network architecture that combines attention mechanisms with convolutional layers. The network introduces an innovative attention mechanism into the YOLOv8 backbone, which effectively enhances the detection accuracy and robustness of small objects through fine-grained and coarse-grained attention routing on feature maps. During the feature fusion stage, we employ adaptive dilated convolution, which dynamically adjusts the dilation rate spatially based on frequency components. This adaptive convolution kernel helps preserve the details of small objects while strengthening their feature representation. It also expands the receptive field, which is beneficial for capturing contextual information and the overall features of small objects. Our method demonstrates an improvement in Average Precision (AP) by 1% on the UA-DETRAC-test dataset and 3% on the VisDrone-test dataset when compared to state-of-the-art methods. The experiments indicate that the new architecture achieves significant performance improvements across various evaluation metrics. To fully leverage the potential of our approach, we conducted extended research on radar–camera systems.