# Pelee: A Real-Time Object Detection System on Mobile Devices

arXiv: https://arxiv.org/abs/1804.06882 · HTML: https://mlsanity.com/paper/1804.06882/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2018.

## Built on by (most cited first, 30 of 56)

- [Distilling Object Detectors via Decoupled Features](https://mlsanity.com/paper/2103.14475/) (2021)
- [A Survey of Modern Deep Learning based Object Detection Models](https://mlsanity.com/paper/2104.11892/) (2021)
- [YOLO5Face: Why Reinventing a Face Detector](https://mlsanity.com/paper/2105.12931/) (2021)
- [Towards Light-weight and Real-time Line Segment Detection](https://mlsanity.com/paper/2106.00186/) (2021)
- [Deep Learning for UAV-based Object Detection and Tracking: A Survey](https://mlsanity.com/paper/2110.12638/) (2021)
- [Network Augmentation for Tiny Deep Learning](https://mlsanity.com/paper/2110.08890/) (2021)
- [Object Detection with Transformers: A Review](https://mlsanity.com/paper/2306.04670/) (2023)
- [Prompt-Guided Zero-Shot Anomaly Action Recognition using Pretrained Deep Skeleton Features](https://mlsanity.com/paper/2303.15167/) (2023)
- [An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applications](https://mlsanity.com/paper/2101.04930/) (2021)
- [The State of Aerial Surveillance: A Survey](https://mlsanity.com/paper/2201.03080/) (2022)
- [Unified Keypoint-based Action Recognition Framework via Structured Keypoint Pooling](https://mlsanity.com/paper/2303.15270/) (2023)
- [NNSplitter: An Active Defense Solution for DNN Model via Automated Weight Obfuscation](https://mlsanity.com/paper/2305.00097/) (2023)
- [Prediction-Guided Distillation for Dense Object Detection](https://mlsanity.com/paper/2203.05469/) (2022)
- [End-to-end Compression Towards Machine Vision: Network Architecture Design and Optimization](https://mlsanity.com/paper/2107.00328/) (2021)
- [Efficient Few-Shot Object Detection via Knowledge Inheritance](https://mlsanity.com/paper/2203.12224/) (2022)
- [DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs](https://mlsanity.com/paper/2403.19588/) (2024)
- [A Unified Light Framework for Real-time Fault Detection of Freight Train Images](https://mlsanity.com/paper/2102.00381/) (2021)
- [Fcaformer: Forward Cross Attention in Hybrid Vision Transformer](https://mlsanity.com/paper/2211.07198/) (2022)
- [Advancing Earth Observation: A Survey on AI-Powered Image Processing in Satellites](https://mlsanity.com/paper/2501.12030/) (2025)
- [Efficient Object Detection in Optical Remote Sensing Imagery via Attention-based Feature Distillation](https://mlsanity.com/paper/2310.18676/) (2023)
- [Tackling the Background Bias in Sparse Object Detection via Cropped Windows](https://mlsanity.com/paper/2106.02288/) (2021)
- [An Empirical Study of Adder Neural Networks for Object Detection](https://mlsanity.com/paper/2112.13608/) (2021)
- [DPNet: Dual-Path Network for Real-time Object Detection with Lightweight Attention](https://mlsanity.com/paper/2209.13933/) (2022)
- [Illicit item detection in X-ray images for security applications](https://mlsanity.com/paper/2305.01936/) (2023)
- [CSL-YOLO: A New Lightweight Object Detection System for Edge Computing](https://mlsanity.com/paper/2107.04829/) (2021)
- [Multi-scale Graph Convolutional Networks with Self-Attention](https://mlsanity.com/paper/2112.03262/) (2021)
- [Efficient Convolutional Neural Networks on Raspberry Pi for Image Classification](https://mlsanity.com/paper/2204.00943/) (2022)
- [MobileDenseNet: A new approach to object detection on mobile devices](https://mlsanity.com/paper/2207.11031/) (2022)
- [Q-YOLO: Efficient Inference for Real-time Object Detection](https://mlsanity.com/paper/2307.04816/) (2023)
- [Visual inspection for illicit items in X-ray images using Deep Learning](https://mlsanity.com/paper/2310.03658/) (2023)

## Abstract (arXiv)

An increasing need of running Convolutional Neural Network (CNN) models on mobile devices with limited computing power and memory resource encourages studies on efficient model design. A number of efficient architectures have been proposed in recent years, for example, MobileNet, ShuffleNet, and MobileNetV2. However, all these models are heavily dependent on depthwise separable convolution which lacks efficient implementation in most deep learning frameworks. In this study, we propose an efficient architecture named PeleeNet, which is built with conventional convolution instead. On ImageNet ILSVRC 2012 dataset, our proposed PeleeNet achieves a higher accuracy and over 1.8 times faster speed than MobileNet and MobileNetV2 on NVIDIA TX2. Meanwhile, PeleeNet is only 66% of the model size of MobileNet. We then propose a real-time object detection system by combining PeleeNet with Single Shot MultiBox Detector (SSD) method and optimizing the architecture for fast speed. Our proposed detection system2, named Pelee, achieves 76.4% mAP (mean average precision) on PASCAL VOC2007 and 22.4 mAP on MS COCO dataset at the speed of 23.6 FPS on iPhone 8 and 125 FPS on NVIDIA TX2. The result on COCO outperforms YOLOv2 in consideration of a higher precision, 13.6 times lower computational cost and 11.3 times smaller model size.