ArticlePeerJ. Computer science2022
Lightweight multi-scale network for small object detection.
Article in PeerJ. Computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 19 citations in OpenAlex.
- Research on a Unified Multi-Type Defect Detection Method for Lithium Batteries Throughout Their Entire Lifecycle Based on Multimodal Fusion and Attention-Enhanced YOLOv8.Sensors (Basel, Switzerland) · 2026Article
- IDOD-YOLOV7: Image-Dehazing YOLOV7 for Object Detection in Low-Light Foggy Traffic Environments.Sensors (Basel, Switzerland) · 2023Article
- Real-time airplane detection using multi-dimensional attention and feature fusion.PeerJ. Computer science · 2023Article
- CAPN: a Combine Attention Partial Network for glove detection.PeerJ. Computer science · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Small object detection is widely used in the real world. Detecting small objects in complex scenes is extremely difficult as they appear with low resolution. At present, many studies have made significant progress in improving the detection accuracy of small objects. However, some of them cannot balance the detection speed and accuracy well. To solve the above problems, a lightweight multi-scale network (LMSN) was proposed to exploit the multi-scale information in this article. Firstly, it explicitly modeled semantic information interactions at every scale via a multi-scale feature fusion unit. Secondly, the feature extraction capability of the network was intensified by a lightweight receptive field enhancement module. Finally, an efficient channel attention module was employed to enhance the feature representation capability. To validate our proposed network, we implemented extensive experiments on two benchmark datasets. The mAP of LMSN achieved 75.76% and 89.32% on PASCAL VOC and RSOD datasets, respectively, which is 5.79% and 11.14% higher than MobileNetv2-SSD. Notably, its inference speed was up to 61 FPS and 64 FPS, respectively. The experimental results confirm the validity of LMSN for small object detection.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.