  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.
