LiteFuseNet:A Lightweight Edge-Aware Multi-Scale Detection for Mobile Robots

Deploying object detection models on resource-constrained embedded devices, such as mobile robots, requires lightweight and efficient network architectures. However, existing lightweight detectors often suffer from insufficient edge representation, redundant cross-scale features, and high inference costs, limiting their applicability. To address these challenges, this paper proposes LiteFuseNet, an efficient detection framework that enhances localization precision and reduces computational overhead. The network leverages multi-scale edge fusion to enhance edge awareness through edge response, and incorporates a hierarchical partial path aggregation with adaptive channel filtering feature fusion to enable efficient cross-layer guidance while reducing redundant information. In addition, it leverages a lightweight shared convolution and decoupled batch normalization head, sharing convolution across scales and using separate batch normalization for each scale to enhance efficiency. Extensive experiments on a self-constructed dataset and public benchmarks demonstrate that LiteFuseNet achieves superior detection performance with significantly reduced parameters and computation, and shows favorable results in mobile robot detection experiments, validating the effectiveness of the proposed network.