Information-Preserving Lightweight Object Detection Network Utilizing Dynamic Gating and Multi-Scale Selection
Visual object detection is essential for environment perception in intelligent robots, automated assembly, unmanned inspection, and industrial detection systems. Although lightweight detectors reduce complexity through compact architectures, fixed convolutional units, and progressive downsampling, their limited scale responses and spatial-detail loss may degrade the localization of scale-varying and boundary-sensitive objects. To address this issue, this paper proposes DGMS-YOLO, an information-preserving lightweight detector built on a one-stage framework. The model improves feature representation through adaptive scale selection and information preservation. Specifically, the Dynamic Gated Multi-Scale Selection (DGMS) module extracts multi-scale features using depthwise convolution branches with different receptive fields and generates content-aware scale weights from the mean and standard deviation of input features. A temperature-scaled softmax and uniform scale prior are further introduced to prevent premature branch-weight concentration during multi-branch training. The Dual Pooling Downsampling (DPD) module combines max pooling, average pooling, and stride convolution to preserve salient responses, regional structures, and learnable semantic features during downsampling. In addition, high-frequency residual calibration estimates edge residuals from low-frequency features and applies lightweight channel gating to compensate for localization-related textures and boundary details. On PASCAL VOC 2007, DGMS-YOLO achieves $\text{7 6. 6 7 \%} \text{m A P}_{50}$ and $\text{5 5. 3 3 \%} \text{m A P}_{50: 95}$ with a parameter count comparable to YOLOv8s, improving it by 1.23 and 2.00 percentage points, respectively. These results demonstrate that dynamic scale selection and information preservation improve detection accuracy and localization quality under a parameter and storage budget comparable to YOLOv8s.