Research on efficient image semantic segmentation algorithm for glass transparent containers based on improved DeepLabV3+

Aiming at the problems of slow segmentation speed and excessive computational complexity in practical engineering applications when using existing models to segment glass transparent containers, an efficient image semantic segmentation algorithm based on improved DeepLabV3+for glass transparent containers is proposed. The proposed algorithm uses the MobileNetV3 network to replace the backbone feature extraction network Xception of the original model, effectively reduces the number of parameters of the semantic segmentation model, improves the ASPP module, introduces the strip pooling module (SPM) and depthwise separable atrous convolution (DCAC), and uses the densely connected multiple receptive field cascade (MRFC) to obtain multi-scale feature information. In the decoding structure, multi-level feature fusion (MLFF) is used to recover the details and levels of features lost during the down sampling process. Adding the spatial attention mechanism module SAM helps refine the target boundaries. The proposed model reasoning speed is 1.5 times faster than DeepLabV3+, and FLOPs are reduced by 71.4%. In addition to a 0.14% decrease in the cuvette's M_iou, the beaker and reagent bottle's M_iou increased by 0.19% and 0.18%, respectively. From the experimental results, we have improved the reasoning speed without completely losing accuracy, achieved the segmentation effect that is comparable to the original model, and well considered the accuracy, computational complexity, and reasoning speed, achieving a good balance between various aspects.