Bojun Liu

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.

The Secretary Bird Optimization Algorithm (SBOA) is a novel swarm-based meta-heuristic that formulates an optimization model by mimicking the secretary bird’s hunting and predator-evasion behaviors, and thus possesses appreciable application potential. Nevertheless, it suffers from an unbalanced exploration–exploitation ratio, difficulty in maintaining population diversity, and a tendency to be trapped in local optima. To eliminate these drawbacks, this paper proposes an SBOA variant (MESBOA) that integrates a multi-population management strategy with an experience-trend guidance strategy. The proposed method is compared with eight advanced basic/enhanced algorithms of different categories on both the CEC2017 and CEC2022 test suites. Experimental results demonstrate that MESBOA delivers faster convergence, more stable robustness and higher accuracy, achieving mean rankings of 2.500 (CEC2022 10-D), 2.333 (CEC2022 20-D), 1.828 (CEC2017 50-D) and 1.931 (CEC2017 100-D). Moreover, engineering constrained optimization problems further verify its applicability to real-world optimization tasks.