By introducing the shared convolution mechanism, LSCD effectively reduces the number of detection heads and enhances the global information fusion ability between feature graphs, …

2024年5月27日 · The LSCD-Head, designed by combining the advantages of GroupNorm and shared convolution, is more lightweight and retains the advantages of detecting small objects.

2) A light-weight detection head architecture, named Lightweight Shared Convolutional Detection (LSCD), is devised. By incorporating a shared convolutional mechanism, it effectively …

In this paper, a lightweight fire detection algorithm based on LSCD-FasterC2f-YOLOv8 is proposed to optimize the real-time and accuracy of traditional target detection models in fire …

提出了一种轻量级共享卷积检测头（LSCD head），利用Group normalization提升检测头定位和分类的性能，并通过共享卷积进行特征交互，减少参数量的同时提高检测精度。

The LSCD-Head, designed by combining the advantages of GroupNorm and shared convolution, is more light-weight and retains the advantages of detecting small objects.

2024年11月25日 · To optimize efficiency and reduce model complexity, we propose the Lightweight Shared Convolutional Detection (LSCD) head. Our approach focuses on using a …

2024年9月7日 · 在YOLOv8中，Head部分负责将Neck部分输出的特征进行进一步处理，以生成最终的目标检测结果。 Head部分的主要功能是将特征图转换为目标检测，分类和关键点检测任 …

2024年8月30日 · Moreover, a lightweight shared convolutional detection head (LSCD head) is meticulously designed to enhance detection efficiency while reducing model size. Ultimately, …

2024年11月8日 · 专栏链接: YOLOv11目标检测创新改进与实战案例. 文章浏览阅读1.5k次。 【YOLO11改进 - 检测头】Detect_LSCD检测头:量化的检测头，进一步提升模型的检测效率和精度.