To solve missing and false detection caused by different fuzzy deformations and insufficient features of target non-motor vehicles due to different heights and angles of detectors under road monitoring, this study proposes a non-motor vehicle detection model based on one-shot aggregation (VovNet) network and deformable convolution. CSPVovNet proposed by the VovNet network combined with the characteristics of the original network is used to replace the original CSPDarknet backbone network for feature extraction. This enhances the reuse of effective features and alleviates the further loss of features of small target objects caused by deep convolution. Deformable convolution is introduced into different network layers to replace the traditional convolution. Training and testing are carried out on the public data set Pascal VOC2007 and the self-built non-motor vehicle data set, respectively. The YOLOv5-C scheme is selected according to the final performance. The improved network selects EIoU_loss as the location loss. The ablation experiment shows that the final improvement improves the network performance, with the final network optimization result being 4.14 percentage points higher than the original YOLOv5s network in terms of mAP, which thus effectively alleviates missing and false detection.