Contrastive Feature Enhancement for Elevated Warehouse Small Target Detection Method

doi:10.3778/j.issn.1002-8331.2307-0273

Abstract

Abstract: In response to the issues of limited target feature information and low classification accuracy in safety helmet detection in elevated warehouse scenarios, a small target contrastive feature enhancement network is proposed. Firstly, a spatial pyramid pooling fast cross layer fusion module is introduced to reduce the loss of target information in the spatial dimension. Then, a small target contrastive feature enhancement module is presented, utilizing dual-path parallel dilated convolutions to capture different receptive fields, and incorporating channel attention to obtain more precise feature information in the channel dimension. Additionally, the large object information in shallow feature maps is weakened by subtracting them from deep feature maps, aiming to enhance the expression of small object features. Finally, an efficient channel attention decoupled detection head is incorporated, separating the detection head into classification and regression branches to learn semantic and positional information of the targets, respectively. Experimental results on the TT100K dataset demonstrate that the proposed method improves the mAP@0.5 compared to the YOLOv5 baseline network by 6.4 percentage points and outperforms YOLOv7 by 1.9 percentage points. Moreover, on a self-built elevated warehouse dataset, the method achieves a 4.9 percentage points improvement in mAP@0.5 compared to the baseline network, and a 6.9 percentage points increase in mAP@0.5 specifically for safety helmets.

Key words: small target detection, elevated warehouse, cross-layer fusion, contrastive feature enhancement, decoupled detection head, YOLOv5

摘要： 针对高架库区场景下安全帽检测中目标特征信息少、分类精度低等问题，提出小目标对比特征增强网络。首先提出快速空间金字塔池化跨层融合模块，减少空间维度上的目标信息丢失。然后提出小目标对比特征增强模块，使用双路并行空洞卷积获取不同感受野，利用通道注意力获取特征图在通道维度上更为精准的特征信息，采用浅层特征图减去深层特征图的方法削弱浅层特征图中大目标信息，以增强小目标特征信息表达。加入高效通道注意力解耦检测头，通过将检测头解耦为分类和回归分支，分别学习目标的语义信息和位置信息。实验结果表明，在TT100K数据集上，所提方法的mAP@0.5比基准网络YOLOv5提高了6.4个百分点，比YOLOv7提高了1.9个百分点。在自建高架库数据集上，所提方法的mAP@0.5相比基准网络提高了4.9个百分点，其中安全帽的mAP@0.5相比基准网络提高了6.9个百分点。

关键词: 小目标检测, 高架库, 跨层融合, 对比特征增强, 解耦检测头, YOLOv5

ZHU He, BIAN Changzhi, ZHANG Jing, WANG Li, LI Xiaoxia, CHEN Yuling. Contrastive Feature Enhancement for Elevated Warehouse Small Target Detection Method[J]. Computer Engineering and Applications, 2024, 60(22): 347-354.

朱贺, 卞长智, 张婧, 王力, 李小霞, 陈禹伶. 对比特征增强的高架库小目标检测方法[J]. 计算机工程与应用, 2024, 60(22): 347-354.

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