# MelNet: A Real-Time Deep Learning Algorithm for Object Detection

arXiv: https://arxiv.org/abs/2401.17972 · HTML: https://mlsanity.com/paper/2401.17972/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 66 references that are in the map.
- First posted on arXiv 31 Jan 2024.

## Builds on (most cited first, 19 of 19)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [Soft-NMS -- Improving Object Detection With One Line of Code](https://mlsanity.com/paper/1704.04503/) (2017)
- [Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark](https://mlsanity.com/paper/2109.14545/) (2021)
- [Dual Path Networks](https://mlsanity.com/paper/1707.01629/) (2017)
- [Hyper-Realist Rendering: A Theoretical Framework](https://mlsanity.com/paper/2401.12853/) (2024)
- [GenPluSSS: A Genetic Algorithm Based Plugin for Measured Subsurface Scattering Representation](https://mlsanity.com/paper/2401.15245/) (2024)
- [LittleYOLO-SPP: A Delicate Real-Time Vehicle Detection Algorithm](https://mlsanity.com/paper/2011.05940/) (2020)
- [A Vehicle Detection Approach using Deep Learning Methodologies](https://mlsanity.com/paper/1804.00429/) (2018)

## Built on by (most cited first, 4 of 4)

- [ExTTNet: A Deep Learning Algorithm for Extracting Table Texts from Invoice Images](https://mlsanity.com/paper/2402.02246/) (2024)
- [LightFFDNets: Lightweight Convolutional Neural Networks for Rapid Facial Forgery Detection](https://mlsanity.com/paper/2411.11826/) (2024)
- [OkanNet: A Lightweight Deep Learning Architecture for Classification of Brain Tumor from MRI Images](https://mlsanity.com/paper/2604.01264/) (2026)
- [TRACK: Telemetry-Based Racing Analysis and Coaching Kit in Sim Racing Games](https://mlsanity.com/paper/2610.10061/) (2026)

## Abstract (arXiv)

In this study, a novel deep learning algorithm for object detection, named MelNet, was introduced. MelNet underwent training utilizing the KITTI dataset for object detection. Following 300 training epochs, MelNet attained an mAP (mean average precision) score of 0.732. Additionally, three alternative models -YOLOv5, EfficientDet, and Faster-RCNN-MobileNetv3- were trained on the KITTI dataset and juxtaposed with MelNet for object detection. The outcomes underscore the efficacy of employing transfer learning in certain instances. Notably, preexisting models trained on prominent datasets (e.g., ImageNet, COCO, and Pascal VOC) yield superior results. Another finding underscores the viability of creating a new model tailored to a specific scenario and training it on a specific dataset. This investigation demonstrates that training MelNet exclusively on the KITTI dataset also surpasses EfficientDet after 150 epochs. Consequently, post-training, MelNet's performance closely aligns with that of other pre-trained models.