Rapid and accurate target detection is one of the inevitable requirements of intelligent construction site. To meet the speed requirements and improve detection accuracy, an improved small object detection (ISOD) network is proposed. The network utilizes an efficient channel attention mechanism to extract features in the backbone and combines the proposed extended scale feature pyramid network to simplify calculations and create additional high-resolution pyramid layers to improve the ability of detecting small targets. To verify the effectiveness of ISOD, experiments are conducted using the proposed Reflective Vest Scene Dataset and Tsinghua-Tencent 100K, achieving 0.425 and 0.635 mAP@0.5\(-\)0.95, respectively, exceeding the SOTA YOLOv7 model, demonstrating its excellent small target detection capability and scalability.

快速、准确的目标检测是智能化施工现场的必然要求之一。为了满足速度要求并提高检测精度，提出了一种改进的小目标检测（ISOD）网络。该网络利用高效的通道注意机制来提取主干中的特征，并结合所提出的扩展尺度特征金字塔网络来简化计算并创建额外的高分辨率金字塔层以提高检测小目标的能力。为了验证ISOD的有效性，使用提出的反光背心场景数据集和清华腾讯100K进行实验，分别达到0.425和0.635 mAP@0.5 \(-\) 0.95，超过SOTA YOLOv7模型，展示了其优秀的小目标检测能力和可扩展性。