Lightweight Multi-Scale Feature Extraction with Fully Connected LMF Layer for Salient Object Detection

Since AlexNet [1] won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012, deep neural networks (DNNs) have rapidly evolved, surpassing traditional machine learning methods in accuracy and becoming the dominant approach in computer vision. By stacking multiple convolu-tional layers, AlexNet enabled the network to learn increasingly complex image features, profoundly influencing subsequent network architectures, such as VGG [2]. However, despite significant performance improvements, DNNs often suffer from an excessive number of parameters and high computational costs, making them challenging to deploy on resource-constrained devices. Moreover, as network depth and complexity increase, performance gains tend to diminish. Consequently, developing efficient neural networks with fewer parameters and reduced computational complexity has become a crucial research direction, driving the growing interest in lightweight network design. Optimization strategies for lightweight networks generally fall into two categories: lightweight model design and model compression. Unlike model compression, which reduces redundancy in pre-trained models, lightweight model design fundamentally lowers computational complexity and parameter count, avoiding potential performance degradation caused by compression techniques. Studies have shown that multi-scale feature learning is essential for enhancing model representation capabilities, particularly in dense prediction tasks such as image segmentation and salient object detection (SOD). Traditional convolutional neural networks (CNNs), including VGG and ResNet [3], achieve multi-scale feature learning by encoding high-level semantic information in deeper layers while preserving low-level details in shallower ones.