# Localization-aware Channel Pruning for Object Detection

arXiv: https://arxiv.org/abs/1911.02237 · HTML: https://mlsanity.com/paper/1911.02237/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2019.

## Built on by (most cited first, 13 of 13)

- [Distilling Object Detectors via Decoupled Features](https://mlsanity.com/paper/2103.14475/) (2021)
- [Performance-aware Approximation of Global Channel Pruning for Multitask CNNs](https://mlsanity.com/paper/2303.11923/) (2023)
- [Cut Inner Layers: A Structured Pruning Strategy for Efficient U-Net GANs](https://mlsanity.com/paper/2206.14658/) (2022)
- [Sauron U-Net: Simple automated redundancy elimination in medical image segmentation via filter pruning](https://mlsanity.com/paper/2209.13590/) (2022)
- [Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision](https://mlsanity.com/paper/2411.01431/) (2024)
- [R-TOSS: A Framework for Real-Time Object Detection using Semi-Structured Pruning](https://mlsanity.com/paper/2303.02191/) (2023)
- [Visual Detector Compression via Location-Aware Discriminant Analysis](https://mlsanity.com/paper/2509.17968/) (2025)
- [Visual Saliency-Guided Channel Pruning for Deep Visual Detectors in Autonomous Driving](https://mlsanity.com/paper/2303.02512/) (2023)
- [CoroNetGAN: Controlled Pruning of GANs via Hypernetworks](https://mlsanity.com/paper/2403.08261/) (2024)
- [Architectural Insights into Knowledge Distillation for Object Detection: A Comprehensive Review](https://mlsanity.com/paper/2508.03317/) (2025)
- [Revisiting Data Augmentation in Model Compression: An Empirical and Comprehensive Study](https://mlsanity.com/paper/2305.13232/) (2023)
- [AlterMOMA: Fusion Redundancy Pruning for Camera-LiDAR Fusion Models with Alternative Modality Masking](https://mlsanity.com/paper/2409.17728/) (2024)
- [UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles](https://mlsanity.com/paper/2501.04213/) (2025)

## Abstract (arXiv)

Channel pruning is one of the important methods for deep model compression. Most of existing pruning methods mainly focus on classification. Few of them conduct systematic research on object detection. However, object detection is different from classification, which requires not only semantic information but also localization information. In this paper, based on discrimination-aware channel pruning (DCP) which is state-of-the-art pruning method for classification, we propose a localization-aware auxiliary network to find out the channels with key information for classification and regression so that we can conduct channel pruning directly for object detection, which saves lots of time and computing resources. In order to capture the localization information, we first design the auxiliary network with a contextual ROIAlign layer which can obtain precise localization information of the default boxes by pixel alignment and enlarges the receptive fields of the default boxes when pruning shallow layers. Then, we construct a loss function for object detection task which tends to keep the channels that contain the key information for classification and regression. Extensive experiments demonstrate the effectiveness of our method. On MS COCO, we prune 70% parameters of the SSD based on ResNet-50 with modest accuracy drop, which outperforms the-state-of-art method.