Autonomous driving systems face significant challenges in deploying object detection models that must balance accuracy, real-time performance, and lightweight design on resource-constrained in-vehicle platforms. However, inadequate multi-scale feature fusion and redundant feature representations in existing methods limit their deployment on such platforms. To address these issues, we propose an Adaptive Multi-Scale Fusion Lightweight Network (AMSFNet) based on YOLOv11. We introduce a Spatial and Channel Reconstruction Attention Fusion (SCRAFusion) mechanism that employs a Mixup–Reconstruct–Fuse strategy to adaptively integrate multi-scale features, thereby enhancing feature representation, particularly for small objects. Building on this mechanism, we develop a C2fSCConv module that incorporates Spatial and Channel Reconstruction Convolution (SCConv) and SCRAFusion to reduce spatial and channel redundancy and enhance feature representation. Furthermore, we formulate a Scale-aware Dynamic IoU (SDIoU) loss that dynamically adjusts bounding-box regression weights based on target size to improve localization across object scales. Experimental results demonstrate that AMSFNet achieves a favorable balance among detection accuracy, model complexity, and real-time performance, reaching 31 FPS on an NVIDIA Jetson Nano (4 GB).
Zhang et al. (Tue,) studied this question.