A MaskFormer EfficientNet instance segmentation approach for crowd counting

In computer vision tasks, object detection is the most significant challenge. Numerous studies on Convolutional neural network based techniques have been extensively utilized in computer vision for the detection of objects. Scale variation, illumination variation or occlusion problems are the most popular challenges in crowd counting. To address this, MaskFormer EfficientNetB7 Instance Segmentation architecture has been proposed that utilized the EfficientNetB7 as the backbone for feature extraction to create efficient and accurate counting of people in challenging scenarios. A basic mask classification model called MaskFormer has been used to predict a series of binary masks, each of which is connected to a single global class for label prediction and EfficientNetB7 has used a compound scaling algorithm that equally scaled each dimension using a predetermined set of scaling coefficients. Experimental findings on the UCF-QNRF, ShanghalTech (Part A and Part B) and Mall datasets have demonstrated that the suggested strategy has provided remarkable outcomes in contrast to existing crowd counting approaches in terms of Mean Absolute Error and Root Mean Squared Error. Therefore, the proposed crowd-counting model has been proven to be more adaptable in different environments or scenarios along with strong generalizability on unseen data.