MonoLI: Precise Monocular 3-D Object Detection for Next-Generation Consumer Electronics for Autonomous Electric Vehicles

In the wake of developments in consumer electronics, electric vehicles are gradually becoming representative carriers of next-generation consumer electronics. Electric vehicles incorporate 5G and artificial intelligence (AI) technologies, which significantly enhance the driving experience. Autonomous driving, as a widely supported feature of most electric vehicles, allows these vehicles to make decisions automatically, reducing safety hazards and enhancing driving efficiency. There has been a growing interest in cost-effective monocular vision solutions to further improve the viability of this technology. However, the limited imaging capabilities of monocular cameras make it challenging to extract explicit depth information from images, which is crucial for locating objects in a scene. In this paper, based on a location-aware attention mechanism and an importance-aware detection head, we propose MonoLI, a monocular 3D object detection method for precisely locating objects. First, the location-aware attention mechanism can perceive location information in both the spatial and channel dimensions. This helps the network learn a more suitable global feature representation for the detection task. Second, the importance-aware detection head considers the difference between auxiliary and target tasks, as well as the importance of different branches in target tasks. In addition, to enhance the applicability and deployability of the method, we introduce partial convolution blocks, which improve the performance and effectively reduce the parameter size and computational complexity. Finally, experiments conducted on the KITTI benchmark dataset show that our method outperforms many well-known baseline methods in terms of 3D object detection and bird’s-eye view (BEV) evaluation.