A Small-Scale Object Detection Algorithm in Intelligent Transportation Scenarios

• Published in: Entropy (Basel, Switzerland) Vol. 26; no. 11; p. 920
• Main Authors: Song, Junzi, Han, Chunyan, Wu, Chenni
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.10.2024; MDPI
• Subjects: Accuracy; Algorithms; Data processing; Datasets; Deep learning; Distance measurement; feature pyramid; Image enhancement; information entropy; intelligent transportation; Methods; Object recognition; Real time; small object detection; Target detection; Telematics; Transportation; Transportation industry; Transportation models; YOLOv4 tiny; information entropy; small object detection; intelligent transportation; YOLOv4 tiny; feature pyramid
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e26110920

In response to the problem of poor detection ability of object detection models for small-scale targets in intelligent transportation scenarios, a fusion method is proposed to enhance the features of small-scale targets, starting from feature utilization and fusion methods. The algorithm is based on the YOLOv4 tiny framework and enhances the utilization of shallow and mid-level features on the basis of Feature Pyramid Network (FPN), improving the detection accuracy of small and medium-sized targets. In view of the problem that the background of the intelligent traffic scene image is cluttered, and there is more redundant information, the Convolutional Block Attention Module (CBAM) is used to improve the attention of the model to the traffic target. To address the problem of data imbalance and prior bounding box adaptation in custom traffic data sets that expand traffic images in COCO and VOC, we propose a Copy-Paste method with an improved generation method and a K-means algorithm with improved distance measurement to enhance the model’s detection ability for corresponding categories. Comparative experiments were conducted on a customized 260-thousand traffic data set containing public traffic images, and the results showed that compared to YOLOv4 tiny, the proposed algorithm improved mAP by 4.9% while still ensuring the real-time performance of the model.