A Lightweight Scene Text Detector with Enhanced Multi-Step Feature Fusion

The field of scene text detection has gained increasing attention in recent years. However, it faces the challenges of high computational overhead. This paper proposes a Lightweight Multi-Step Feature Fusion Network (LMFFNet), an enhanced model designed to reduce computational burden in scene text detection tasks. Based on the lightweight MobileNetV3 backbone network, the proposed network combines the Triplet Attention Feature Enhancement Module (TFEM) and the Dynamic Feature Fusion Module (DFFM) to achieve efficient scene text detection. The TFEM expands the receptive field of the model and better utilizes features of various scales. The DFFM, which incorporates Convolutional Block Attention Module (CBAM), adaptively fuses features of different scales to improve the performance and robustness of the model. In the loss function, Dice Loss is used to address the imbalance between positive and negative samples. The proposed LMFFNet achieves a detection accuracy of 90.7% on the ICDAR2015 dataset, with a 91.7% reduction in parameter count and a 70.0% reduction in overall model overhead compared to the baseline model DBNet (ResNet-50).The effectiveness of the lightweight scene text detector is verified.