An Improved Lightweight Network Using Attentive Feature Aggregation for Object Detection in Autonomous Driving

Abstract

Object detection, a more advanced application of computer vision than image classification, utilizes deep neural networks to predict objects in an input image and determine their locations through bounding boxes. The field of artificial intelligence has increasingly focused on the demands of autonomous driving, which require both high accuracy and fast inference speeds. This research paper aims to address this demand by introducing an efficient lightweight network for object detection specifically designed for self-driving vehicles. The proposed network, named MobDet3, incorporates a modified MobileNetV3 as its backbone, leveraging its lightweight convolutional neural network algorithm to extract and aggregate image features. Furthermore, the network integrates altered techniques in computer vision and adjusts to the most recent iteration of the PyTorch framework. The MobDet3 network enhances not only object positioning ability but also the reusability of feature maps across different scales. Extensive evaluations were conducted to assess the effectiveness of the proposed network, utilizing an autonomous driving dataset, as well as large-scale everyday human and object datasets. These evaluations were performed on NXP BlueBox 2.0, an advanced edge development platform designed for autonomous vehicles. The results demonstrate that the proposed lightweight object detection network achieves a mean precision of up to 58.30% on the BDD100K dataset and a high inference speed of up to 88.92 frames per second on NXP BlueBox 2.0, making it well-suited for real-time object detection in autonomous driving applications.