In the field of multimodal robotics, achieving comprehensive and accurate perception of the surrounding environment is a highly sought-after objective. However, current methods still have limitations in motion keypoint detection, especially in scenarios involving small target detection and complex scenes. To address these challenges, we propose an innovative approach known as YOLOv8-PoseBoost. This method introduces the Channel Attention Module (CBAM) to enhance the network’s focus on small targets, thereby increasing sensitivity to small target individuals. Additionally, we employ multiple scale detection heads, enabling the algorithm to comprehensively detect individuals of varying sizes in images. The incorporation of cross-level connectivity channels further enhances the fusion of features between shallow and deep networks, reducing the rate of missed detections for small target individuals. We also introduce a Scale Invariant Intersection over Union (SIoU) redefined bounding box regression localization loss function, which accelerates model training convergence and improves detection accuracy. Through a series of experiments, we validate YOLOv8-PoseBoost’s outstanding performance in motion keypoint detection for small targets and complex scenes. This innovative approach provides an effective solution for enhancing the perception and execution capabilities of multimodal robots. It has the potential to drive the development of multimodal robots across various application domains, holding both theoretical and practical significance.