mlsanityData to 7 Oct 2026

LostNet: A smart way for lost and find

arXiv 2301.02277 ↗First posted 5 Jan 2023cs.CV cs.AI eess.IVcited by 0

Not on the map. Why?

Abstract (arXiv)

Due to the enormous population growth of cities in recent years, objects are frequently lost and unclaimed on public transportation, in restaurants, or any other public areas. While services like Find My iPhone can easily identify lost electronic devices, more valuable objects cannot be tracked in an intelligent manner, making it impossible for administrators to reclaim a large number of lost and found items in a timely manner. We present a method that significantly reduces the complexity of searching by comparing previous images of lost and recovered things provided by the owner with photos taken when registered lost and found items are received. In this research, we will primarily design a photo matching network by combining the fine-tuning method of MobileNetv2 with CBAM Attention and using the Internet framework to develop an online lost and found image identification system. Our implementation gets a testing accuracy of 96.8% using only 665.12M GLFOPs and 3.5M training parameters. It can recognize practice images and can be run on a regular laptop.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 46 references that are in the map. First posted on arXiv 5 Jan 2023.

Builds on

14 of 46 references.

References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2016Densely Connected Convolutional Networkscited by 6,691
  5. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  6. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  7. 2018CBAM: Convolutional Block Attention Modulecited by 2,463
  8. 2019Searching for MobileNetV3cited by 1,984
  9. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,509
  10. 2019A Comprehensive Survey on Transfer Learningcited by 1,154
  11. 2018ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designcited by 1,132
  12. 2015How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?cited by 45
2 more
  1. 2021Convolutional Hough Matching Networkscited by 43
  2. 2022R2FD2: Fast and Robust Matching of Multimodal Remote Sensing Image via Repeatable Feature Detector and Rotation-invariant Feature Descriptorcited by 8

Built on by

Not cited yet.