TL;DR
LittleYOLO-SPP is a lightweight, real-time vehicle detection model based on YOLOv3-tiny, enhanced with spatial pyramid pooling and improved loss functions, achieving high accuracy in complex scenes.
Contribution
This paper introduces LittleYOLO-SPP, a novel real-time vehicle detection network that integrates spatial pyramid pooling into YOLOv3-tiny to improve accuracy and speed.
Findings
Achieves 77.44% mAP on PASCAL VOC
Achieves 52.95% mAP on MS COCO
Detects vehicles effectively in various weather and video conditions
Abstract
Vehicle detection in real-time is a challenging and important task. The existing real-time vehicle detection lacks accuracy and speed. Real-time systems must detect and locate vehicles during criminal activities like theft of vehicle and road traffic violations with high accuracy. Detection of vehicles in complex scenes with occlusion is also extremely difficult. In this study, a lightweight model of deep neural network LittleYOLO-SPP based on the YOLOv3-tiny network is proposed to detect vehicles effectively in real-time. The YOLOv3-tiny object detection network is improved by modifying its feature extraction network to increase the speed and accuracy of vehicle detection. The proposed network incorporated Spatial pyramid pooling into the network, which consists of different scales of pooling layers for concatenation of features to enhance network learning capability. The Mean square…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
Methods(TravEL!!Guide)How Do I File a Claim with Expedia? · Tanh Activation · + ( 1 ) ⟷ 888 ⟷ ( 829 ) ⟷ 0881 How do I file a claim with Expedia? · Spatial Pyramid Pooling
