To address the issues of the low detection accuracy, exorbitant number of parameters, and extensive computations in the existing YOLOv8s vehicle detection model, a lightweight car detection-YOLO (LCD-YOLO) algorithm based on improved YOLOv8s is proposed for lightweight vehicle target detection. The algorithm applies frequency-adaptive dilated convolution (FADC) to optimize the cross-range partial (CSP) bottleneck with two convolutions (C2f) in YOLOv8s to enhance feature fusion ability. Shared convolutional layers reduce the number of network convolutions and parameters, thereby achieving a lightweight model. Through the dynamic focusing of the bounding box regression loss calculation method, this model can effectively improve the network's ability to detect occluded overlapping targets and improve the accuracy of border detection. Experiments on the KITTI dataset show that the average detection accuracy of the proposed algorithm is improved to 95.1%, which is 2.9% higher than that of the YOLOv8s algorithm, while reducing the number of network parameters by 14.9% and amount of computation by 10.9%, which can better satisfy the actual detection needs of vehicles.