AFESSD: Object Detector for Natural Gas Pipeline Construction Scene

Abstract

Object detection is a challenging task in computer vision, which aims to classify and locate objects. SSD (Single Shot MultiBox Detector) is one of the classical algorithms in the current object detection field, which uses multiple feature maps with different scales to detect all objects in an image. Based on SSD, in this paper, we propose an improved AFESSD for object detection in natural gas pipeline construction scene: Firstly, the attention mechanism module is introduced; Secondly, Feature Fusion Block and Feature Enhancement Block are designed to achieve high-to-low level feature information fusion and enhancement. Finally, we verify AFESSD on two object detection datasets of natural gas pipeline construction scene. Experimental results on the first dataset demonstrate that SSD and AFESSD achieve 81.35% and 83.13% mean average precision (mAP) respectively. On the second dataset, SSD and AFESSD achieve 50.55% and 50.99% mAP respectively. Therefore, AFESSD improves SSD’s object detection accuracy in natural gas pipeline construction scene.