Abstract
To solve the problems of a poor manual garbage sorting environment, including heavy tasks and low sorting efficiency, we propose the Lightweight Feature Fusion Single Shot Multibox Detector (L-SSD) algorithm to realize intelligent trash classification and recognition. Since waste has a small volume and the image resolution of garbage is always low, the algorithm that we propose is an enhanced single shot multibox detector (SSD) with a lightweight and novel feature fusion module. This SSD can significantly improve the performance of rubbish detection. In this feature fusion module, features from different layers with different scales are connected in series. A new feature pyramid was generated by using downsampling blocks, which will be fed to appointed multibox detectors to predict the final detection results. Due to the extremely unbalanced ratio of positive samples to negative samples, which leads to a low accuracy of SSD, Focal Loss using balanced cross-entropy is employed, which is provided by easy examples that corresponds to difficult samples with a decline in the loss weight. Thus, the training is biased towards meaningful samples. We have replaced the backbone network of VGG16 with ResNet-101 to achieve more accurate detection. We analyzed the performance of a nonmaximum suppression (NMS) algorithm and discovered that Soft-NMS was more suitable for learning better image representations. The strategy of Soft-NMS is to suppress the undesirable detection box rather than remove it completely. The experimental results show that the L-SSD exceeds a large number of state-of-the-art object detection algorithms in both accuracy and speed.
Highlights
With a rapidly expanding economy, the amount of municipal solid waste has increased rapidly [1]
This paper is aimed at the urgent need for effective refuse classification to address the problems of a poor manual litter sorting environment, heavy tasks and low sorting efficiency
L-single shot multibox detector (SSD) is an enhanced SSD, in which a lightweight but efficient feature fusion module is applied to its framework
Summary
With a rapidly expanding economy, the amount of municipal solid waste has increased rapidly [1]. By addressing current problems with manual sorting, such as poor waste environment, heavy tasks and low sorting efficiency, intelligent and automated debris sorting can reduce labor costs, improve the reuse ratio of recyclable resources and help to rapidly achieve the goal of ecological construction [2]. This method can be fully utilized in practical applications. We could apply it to the garbage identification and classification of intelligent trash cans or the process of garbage sorting in large garbage dumps to reduce the burden of manual classification.
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