MV3-YOLO: A MobileNetV3-Based Lightweight Variant of YOLO for Efficient Object Detection

Efficient object detection is needed in automated driving and edge perception. In these scenarios, a detector must work under limits on latency, power, and memory. YOLOv8 is a strong real-time baseline, but its computation can still be high for compact deployment. This paper proposes MV3-YOLO, a lightweight YOLOv8 variant with a stage-wise hybrid backbone. The early Conv/C2f stages are kept to retain low-level spatial details. Lightweight modules are placed in deeper stages, where feature maps are smaller and redundant computation is more common. C2fMixed is used at the stride-16 stage to balance feature capacity and cost. C2fGhostis used at the deepest stage to generate high-level features with fewer parameters. The YOLOv8 neck and head are kept unchanged for stable multi-scale fusion. On the KITTI validation set, MV3-YOLO reaches mAP@0.5 = 0.859 and mAP@0.5:0.95 = 0.610 with only 2.53 M parameters and 6.6 GFLOPs. Compared with YOLOv8n, it reduces parameters by 19.7% and GFLOPs by 25.0% while improving mAP@0.5 by 1.66% and mAP@0.5:0.95 by 1.50%. On COCO val2017, MV3-YOLO obtains 38.4 mAP@0.5:0.95, which is higher than the YOLOv8n reference result and close to YOLOv10n. These results show that MV3-YOLO reduces deployment cost while keeping competitive detection accuracy.