Performance Analysis of Object Detection Algorithm for Intelligent Traffic Surveillance System

B, Chethan Kumar; Punitha, R.; Mohana,

doi:10.1109/icirca48905.2020.9182793

Cited by 17 publications

(1 citation statement)

References 11 publications

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“…However, they usually cannot accurately detect small targets, since rich feature representations are hard to learn from low-resolution small objects. Small object detection is widely common in real life applications, for instance, intelligent monitoring ( Kumar, Punitha et al, 2020 ), medical treatment ( Ganatra, 2021 ) and autonomous driving ( Feng et al, 2020 ). However, it is difficult to detect small objects in complex real-world scenes due to the low resolution and heavy occlusion.…”

Section: Introductionmentioning

confidence: 99%

Lightweight multi-scale network for small object detection

Zhou

2022

PeerJ Computer Science

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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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Section: Introductionmentioning

confidence: 99%