# Rapid Salient Object Detection with Difference Convolutional Neural Networks Source: [Rapid Salient Object Detection with Difference Convolutional Neural Networks]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:56:50.936Z Authors: Zhuo Su, Li Liu, Matthias M\\"uller, Jiehua Zhang, Diana Wofk, Ming-Ming Cheng, Matti Pietik\\"ainen Published: 2025-07-01T20:41:05.000Z ## Abstract This paper addresses the challenge of deploying salient object detection (SOD) on resource-constrained devices with real-time performance. While recent advances in deep neural networks have improved SOD, existing top-leading models are computationally expensive. We propose an efficient network design that combines traditional wisdom on SOD and the representation power of modern CNNs. Like biologically-inspired classical SOD methods relying on computing contrast cues to determine saliency of image regions, our model leverages Pixel Difference Convolutions (PDCs) to encode the feature contrasts. Differently, PDCs are incorporated in a CNN architecture so that the valuable contrast cues are extracted from rich feature maps. For efficiency, we introduce a difference convolution reparameterization (DCR) strategy that embeds PDCs into standard convolutions, eliminating computation and parameters at inference. Additionally, we introduce SpatioTemporal Difference Convolution (STDC) for video SOD, enhancing the standard 3D convolution with spatiotemporal contrast capture. Our models, SDNet for image SOD and STDNet for video SOD, achieve significant improvements in efficiency-accuracy trade-offs. On a Jetson Orin device, our models with $\<$ 1M parameters operate at 46 FPS and 150 FPS on streamed images and videos, surpassing the second-best lightweight models in our experiments by more than $2\\times$ and $3\\times$ in speed with superior accuracy. Code will be available at https://github.com/hellozhuo/stdnet.git. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [RGB-D Video Salient Object Detection on ViDSOD-100](): RGB-D Video Salient Object Detection; ViDSOD-100; S\_alpha: 69.8. - [RGB-D Video Salient Object Detection on DViSal](): RGB-D Video Salient Object Detection; DVisal; S\_alpha: 64.6. - [RGB-D Video Salient Object Detection on RDVS](): RGB-D Video Salient Object Detection; RDVS; S\_alpha: 59.5. ## Original sources and implementation [Original paper]() [PDF]()