# Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks arXiv: https://arxiv.org/abs/2007.15818 ยท HTML: https://mlsanity.com/paper/2007.15818/ ## Key facts - Cited by 28 papers in the map (data through 14 Oct 2026). - First posted on arXiv 31 Jul 2020. ## Built on by (most cited first, 28 of 28) - [Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges](https://mlsanity.com/paper/2103.04505/) (2021) - [Supervised Compression for Resource-Constrained Edge Computing Systems](https://mlsanity.com/paper/2108.11898/) (2021) - [BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing](https://mlsanity.com/paper/2201.02693/) (2022) - [SC2 Benchmark: Supervised Compression for Split Computing](https://mlsanity.com/paper/2203.08875/) (2022) - [SplitEE: Early Exit in Deep Neural Networks with Split Computing](https://mlsanity.com/paper/2309.09195/) (2023) - [SAGE: A Split-Architecture Methodology for Efficient End-to-End Autonomous Vehicle Control](https://mlsanity.com/paper/2107.10895/) (2021) - [Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency](https://mlsanity.com/paper/2311.05739/) (2023) - [Communication-oriented Model Fine-tuning for Packet-loss Resilient Distributed Inference under Highly Lossy IoT Networks](https://mlsanity.com/paper/2112.09407/) (2021) - [I-SPLIT: Deep Network Interpretability for Split Computing](https://mlsanity.com/paper/2209.11607/) (2022) - [Single-Training Collaborative Object Detectors Adaptive to Bandwidth and Computation](https://mlsanity.com/paper/2105.00591/) (2021) - [A Multi-task Supervised Compression Model for Split Computing](https://mlsanity.com/paper/2501.01420/) (2025) - [A Low-Complexity Approach to Rate-Distortion Optimized Variable Bit-Rate Compression for Split DNN Computing](https://mlsanity.com/paper/2208.11596/) (2022) - [Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges](https://mlsanity.com/paper/2411.18199/) (2024) - [Leveraging the Edge and Cloud for V2X-Based Real-Time Object Detection in Autonomous Driving](https://mlsanity.com/paper/2308.05234/) (2023) - [On the Impact of Black-box Deployment Strategies for Edge AI on Latency and Model Performance](https://mlsanity.com/paper/2403.17154/) (2024) - [Distributed Inference on Mobile Edge and Cloud: An Early Exit based Clustering Approach](https://mlsanity.com/paper/2410.05338/) (2024) - [SpikeBottleNet: Spike-Driven Feature Compression Architecture for Edge-Cloud Co-Inference](https://mlsanity.com/paper/2410.08673/) (2024) - [Federated Split Learning with Improved Communication and Storage Efficiency](https://mlsanity.com/paper/2507.15816/) (2025) - [Resilience of Entropy Model in Distributed Neural Networks](https://mlsanity.com/paper/2403.00942/) (2024) - [On the Impact of White-box Deployment Strategies for Edge AI on Latency and Model Performance](https://mlsanity.com/paper/2411.00907/) (2024) - [LimitNet: Progressive, Content-Aware Image Offloading for Extremely Weak Devices & Networks](https://mlsanity.com/paper/2504.13736/) (2025) - [FedSEA-LLaMA: A Secure, Efficient and Adaptive Federated Splitting Framework for Large Language Models](https://mlsanity.com/paper/2505.15683/) (2025) - [An Efficient Semantic Segmentation Decoder for In-Car or Distributed Applications](https://mlsanity.com/paper/2510.16747/) (2025) - [Model Compression for Resource-Constrained Mobile Robots](https://mlsanity.com/paper/2207.10082/) (2022) - [Distributed Inference on Mobile Edge and Cloud: A Data-Cartography based Clustering Approach](https://mlsanity.com/paper/2412.16616/) (2024) - [Rethinking Inference Placement for Deep Learning across Edge and Cloud Platforms: A Multi-Objective Optimization Perspective and Future Directions](https://mlsanity.com/paper/2510.22909/) (2025) - [SPADE: Speculative Decoding for Precise and Low Cost Distributed Edge Cloud Inference](https://mlsanity.com/paper/2608.13076/) (2026) - [Distributed Semantic Segmentation With Improved Rate-Distortion Trade-Off](https://mlsanity.com/paper/2608.28684/) (2026) ## Abstract (arXiv) The edge computing paradigm places compute-capable devices - edge servers - at the network edge to assist mobile devices in executing data analysis tasks. Intuitively, offloading compute-intense tasks to edge servers can reduce their execution time. However, poor conditions of the wireless channel connecting the mobile devices to the edge servers may degrade the overall capture-to-output delay achieved by edge offloading. Herein, we focus on edge computing supporting remote object detection by means of Deep Neural Networks (DNNs), and develop a framework to reduce the amount of data transmitted over the wireless link. The core idea we propose builds on recent approaches splitting DNNs into sections - namely head and tail models - executed by the mobile device and edge server, respectively. The wireless link, then, is used to transport the output of the last layer of the head model to the edge server, instead of the DNN input. Most prior work focuses on classification tasks and leaves the DNN structure unaltered. Herein, our focus is on DNNs for three different object detection tasks, which present a much more convoluted structure, and modify the architecture of the network to: (i) achieve in-network compression by introducing a bottleneck layer in the early layers on the head model, and (ii) prefilter pictures that do not contain objects of interest using a convolutional neural network. Results show that the proposed technique represents an effective intermediate option between local and edge computing in a parameter region where these extreme point solutions fail to provide satisfactory performance. The code and trained models are available at https://github.com/yoshitomo-matsubara/hnd-ghnd-object-detectors .