LSOD-YOLOv8: Enhancing YOLOv8n with New Detection Head and Lightweight Module for Efficient Cigarette Detection - Yijie Huang; Huimin Ouyang; Xiaodong Miao (Applied Sciences)
Cigarette detection is a crucial component of public safety management. However, detecting such small objects poses significant challenges due to their size and limited feature points. To enhance the accuracy of small target detection, we propose a novel small object detection model, LSOD-YOLOv8 (Lightweight Small Object Detection using YOLOv8). First, we introduce a lightweight adaptive weight downsampling module in the backbone layer of YOLOv8 (You Only Look Once version 8), which not only mitigates information loss caused by conventional convolutions but also reduces the overall parameter count of the model. Next, we incorporate a P2 layer (Pyramid Pooling Layer 2) in the neck of YOLOv8, blending the concepts of shared convolutional information and independent batch normalization to design a P2-LSCSBD (P2 Layer-Lightweight Shared Convolutional and Batch Normalization-based Small Object Detection) detection head. Finally, we propose a new loss function, WIMIoU (Weighted Intersection over Union with Inner, Multi-scale, and Proposal-aware Optimization), by combining the ideas of WiseIoU (Wise Intersection over Union), InnerIoU (Inner Intersection over Union), and MPDIoU (Mean Pairwise Distance Intersection over Union), resulting in a significant accuracy improvement without any loss in performance. Our experiments demonstrate that LSOD-YOLOv8 enhances detection accuracy for cigarette detection specifically.