Tiny object detection model based on competitive multi-layer neural network (TOD-CMLNN)
Tiny Object Detection (TOD) is a fundamental and difficult task in computer vision. Current state-of-the-art detectors like RCNN, Fast RCNN, Faster RCNN, SSD, and YOLO can't find small objects using single-stage or multi-stage methods. With the exponential growth of deep learning, several resea...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-05-01
|
| Series: | Intelligent Systems with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266730532300042X |
| _version_ | 1797804524625199104 |
|---|---|
| author | Sachin Chirgaiya Anand Rajavat |
| author_facet | Sachin Chirgaiya Anand Rajavat |
| author_sort | Sachin Chirgaiya |
| collection | DOAJ |
| description | Tiny Object Detection (TOD) is a fundamental and difficult task in computer vision. Current state-of-the-art detectors like RCNN, Fast RCNN, Faster RCNN, SSD, and YOLO can't find small objects using single-stage or multi-stage methods. With the exponential growth of deep learning, several researchers have drawn attention to advances in tiny object detection approaches. This study proposes a TOD-CMLNN (Tiny Object Detection Competitive Multi-Layer Neural Network) architecture with three sub components first competitive multi-layer network, second TOD auxiliary and third multi-level continue features aggregation for accurately detecting small objects. Competitive learning for object detection is the basis of the proposed architecture. Comparison study with existing RCNN, Fast RCNN, Faster RCNN, SSD and YOLO shows significant improvement in the results. TOD-CMLNN receives 72.46 % accuracy in terms of mAP which is impressive as compared to state-of-the-art detectors. |
| first_indexed | 2024-03-13T05:38:29Z |
| format | Article |
| id | doaj.art-4b3775dbea234293b2aa535f65c8f5e0 |
| institution | Directory Open Access Journal |
| issn | 2667-3053 |
| language | English |
| last_indexed | 2024-03-13T05:38:29Z |
| publishDate | 2023-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj.art-4b3775dbea234293b2aa535f65c8f5e02023-06-14T04:34:51ZengElsevierIntelligent Systems with Applications2667-30532023-05-0118200217Tiny object detection model based on competitive multi-layer neural network (TOD-CMLNN)Sachin Chirgaiya0Anand Rajavat1Corresponding author.; Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore, MP, IndiaShri Vaishnav Vidyapeeth Vishwavidyalaya, Indore, MP, IndiaTiny Object Detection (TOD) is a fundamental and difficult task in computer vision. Current state-of-the-art detectors like RCNN, Fast RCNN, Faster RCNN, SSD, and YOLO can't find small objects using single-stage or multi-stage methods. With the exponential growth of deep learning, several researchers have drawn attention to advances in tiny object detection approaches. This study proposes a TOD-CMLNN (Tiny Object Detection Competitive Multi-Layer Neural Network) architecture with three sub components first competitive multi-layer network, second TOD auxiliary and third multi-level continue features aggregation for accurately detecting small objects. Competitive learning for object detection is the basis of the proposed architecture. Comparison study with existing RCNN, Fast RCNN, Faster RCNN, SSD and YOLO shows significant improvement in the results. TOD-CMLNN receives 72.46 % accuracy in terms of mAP which is impressive as compared to state-of-the-art detectors.http://www.sciencedirect.com/science/article/pii/S266730532300042XCompetitive multi-layer neural networkComputer visionTiny object detectionDeep learning |
| spellingShingle | Sachin Chirgaiya Anand Rajavat Tiny object detection model based on competitive multi-layer neural network (TOD-CMLNN) Intelligent Systems with Applications Competitive multi-layer neural network Computer vision Tiny object detection Deep learning |
| title | Tiny object detection model based on competitive multi-layer neural network (TOD-CMLNN) |
| title_full | Tiny object detection model based on competitive multi-layer neural network (TOD-CMLNN) |
| title_fullStr | Tiny object detection model based on competitive multi-layer neural network (TOD-CMLNN) |
| title_full_unstemmed | Tiny object detection model based on competitive multi-layer neural network (TOD-CMLNN) |
| title_short | Tiny object detection model based on competitive multi-layer neural network (TOD-CMLNN) |
| title_sort | tiny object detection model based on competitive multi layer neural network tod cmlnn |
| topic | Competitive multi-layer neural network Computer vision Tiny object detection Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S266730532300042X |
| work_keys_str_mv | AT sachinchirgaiya tinyobjectdetectionmodelbasedoncompetitivemultilayerneuralnetworktodcmlnn AT anandrajavat tinyobjectdetectionmodelbasedoncompetitivemultilayerneuralnetworktodcmlnn |