SEAT-YOLO: A squeeze-excite and spatial attentive you only look once architecture for shadow detection

Abstract

Recently, several deep learning-based architectures have been utilized for the recognition of shadow regions in images and videos with the aim of classification and segmentation of shadow instances. The present work aims towards solving the problem of shadow recognition as a detection and regression problem. To solve the problem of shadow detection in images and videos, we have developed SEAT-YOLO, a deep learning-based shadow detection architecture. The backbone network of the proposed architecture is based on convolution layers, squeeze-excite blocks, spatial attention module, and spatial pyramid pooling layer and the detection network utilizes YOLO detection heads. The proposed SEAT-YOLO architecture is trained and tested on the publicly available SBU shadow detection dataset. For shadow detection on the SBU dataset, it achieved a mAP value of 59.53 %. Furthermore, the proposed SEAT-YOLO is capable of detecting small shadow regions with high accuracy which is a challenge with most deep learning-based detection architectures. Moreover, the other advantage of the proposed SEAT-YOLO is its ability to detect multiple shadow regions in a single image by drawing precise bounding boxes for overlapping shadows. The proposed SEAT-YOLO architecture has high practical implications in driverless vehicles for detection of shadow regions and take effective driving decisions.