ArticleSensors (Basel, Switzerland)2020
Convolutional Neural Networks-Based Object Detection Algorithm by Jointing Semantic Segmentation for Images.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Artificial Intelligence in Clinical Medicine: Challenges Across Diagnostic Imaging, Clinical Decision Support, Surgery, Pathology, and Drug Discovery.Clinics and practice · 2025Review
- A Style Transfer-Based Fast Image Quality Assessment Method for Image Sensors.Sensors (Basel, Switzerland) · 2025Article
- An optimized domain-specific shrimp detection architecture integrating conditional GAN and weighted ensemble learning.Scientific reports · 2025Article
- Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging.Diagnostics (Basel, Switzerland) · 2023Review
- A Lightweight Semantic Segmentation Algorithm Based on Deep Convolutional Neural Networks.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
In recent years, increasing image data comes from various sensors, and object detection plays a vital role in image understanding. For object detection in complex scenes, more detailed information in the image should be obtained to improve the accuracy of detection task. In this paper, we propose an object detection algorithm by jointing semantic segmentation (SSOD) for images. First, we construct a feature extraction network that integrates the hourglass structure network with the attention mechanism layer to extract and fuse multi-scale features to generate high-level features with rich semantic information. Second, the semantic segmentation task is used as an auxiliary task to allow the algorithm to perform multi-task learning. Finally, multi-scale features are used to predict the location and category of the object. The experimental results show that our algorithm substantially enhances object detection performance and consistently outperforms other three comparison algorithms, and the detection speed can reach real-time, which can be used for real-time detection.
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Registered trials
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