Lightweight object detection algorithms are crucial in the field of computer vision, directly affecting whether computer vision algorithms can be deployed on resource-constrained devices and meet the real-time requirements of daily life. To address the above problems, this paper proposes a lightweight object detection algorithm LAD-YOLO based on improved YOLOv8. First, we optimize the point-wise convolution in depthwise separable convolution to enhance the model's learning ability, introduce depthwise separable convolution into the backbone network and neck network to reduce the model size, and construct a lightweight detection head. Meanwhile, the LSKA (Large Separable Kernel Attention) mechanism is introduced to help the model capture multi-scale information and achieve better detection performance. Extensive experiments conducted on the VOC dataset show that the proposed LAD-YOLO algorithm improves the precision (P) and mAP0.5:0.95 by 2.5% and 1.8% respectively compared with YOLOv8n, while maintaining lower parameters and computational complexity.
Wan et al. (Fri,) studied this question.
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