Accelerating Object Detection by Erasing Background Activations

Recent advances in deep learning have enabled complex real-world use cases comprised of multiple vision tasks and detection tasks are being shifted to the edge side as a pre-processing step of the entire workload. However, since running a deep model on resource-constraint devices is challenging, the design of an efficient network is demanded. In this paper, we present an objectness-aware object detection method to accelerate detection speed by circumventing feature map computation on background regions where target objects don't exist. To accomplish this goal, we incorporate a light-weight objectness mask generation (OMG) network in front of an object detection (OD) network so that it can zero out background areas of an input image before being fed into the OD network. The inference speed, therefore, can be expedited with sparse convolution. By switching background areas to zeros for entire activations, the average number of zero values on MobileNetV2-SSDLite with ReLU activation is increased further, from 36% to 68% during inference step, which reduces 37.89\% MAC with negligible accuracy drop on MS-COCO. Moreover, experimental results also show similar trends in heavy networks such as VGG and RetinaNet with ResNet101, and an additional dataset, PASCAL VOC. The code will be released.