Lightweight Condition-Constrained Salient Object Detection Based on Elastic Pixel Difference Convolution.
TL;DR
The core innovation lies in a novel elastic pixel differential convolution operator, which flexibly captures microscopic gradient cues to effectively coordinate high-level semantics with low-level details during inference with zero additional computational overhead.
Abstract
Visual degradation caused by adverse meteorological conditions, such as low light and rain, significantly hinders the deployment of salient object detection (SOD) on edge devices. Existing methods often rely on computationally expensive restoration preprocessing or complex feature stacking, making real-time inference difficult. To address these challenges, this article proposes a novel lightweight model, termed the robust elastic adaptive difference network (READNet). The core innovation lies in a novel elastic pixel differential convolution operator, which flexibly captures microscopic gradient cues to effectively coordinate high-level semantics with low-level details. This operator is further embedded into an inverted residual block through a multibranch structural reparameterization, enabling structure-aware feature enhancement during inference with zero additional computational overhead. Furthermore, to mitigate nonuniformly distributed environmental noise, a condition-adaptive dual gate is introduced. The module innovatively integrates second-order variance statistics and contextual difference mechanisms to adaptively recalibrate features across both channel and spatial dimensions. Experimental results demonstrate that READNet achieves state-of-the-art performance on challenging benchmarks, validating its superior parameter efficiency and suitability for real-time applications. The source code is publicly available at https://github.com/TurnHug/READNet.git.