Speed-Oriented Lightweight Salient Object Detection in Optical Remote Sensing Images

The lightweight model for salient object detection in optical remote sensing images (SOD-RSI) is a recent emerging topic. Due to the complexity of the task, recently published works have achieved effective model compression but have not yet achieved the desired detection speed. To truly release the detection speed of lightweight models while ensuring a favorable accuracy-efficiency tradeoff, we propose a new speed-oriented lightweight SOD-RSI network (SOLNet), which has significant advantages in detection speed. Specifically, we design a lightweight group attention (LGA) module to deconstruct-interact-recombine channel features and an enhanced dynamic encoding (EDE) module for dynamically capturing spatial information. On this basis, the dynamically enhanced aggregation module (DEAM) is further proposed, which mines the intrinsic correlation of feature information by decoding high-level feature maps, eliminating the need to pay additional attention to other scales. SOLNet completes lightweight and efficient decoding through simple cascade aggregation operations. Notably, we also propose an evaluation strategy that takes both speed and accuracy into account, extending a novel lightweight gain (Lg) metric for SOD-RSI. This not only effectively reveals the under-gain issue of lightweight models but also provides theoretical support for the evaluation of subsequent lightweight works. Experimental results on the challenging EORSSD and ORSSD datasets show that SOLNet achieves significant speed improvements and is the state-of-the-art (SOTA) lightweight SOD-RSI method. The code is available at https://github.com/SpiritAshes/SOLNet.