Food object detection and instance segmentation are critical in many applications such as dietary management or food intake monitoring. Food image recognition poses different challenges, such as the existence of a large number of classes, a high inter-class similarity, and high intra-class variance. This, along with the traditional problems associated with object detection and instance segmentation make this a very complex computer vision task. Real-world food datasets generally suffer from long-tailed and fine-grained distributions. However, the recent literature fails to address food detection in this regard. In this research, we propose a novel two-stage object detector, which we call Strong LOng-tailed Food object Detection and instance Segmentation (SLOF-DS), to tackle the long-tailed nature of food images. In addition, a multi-task based framework, which exploits different sources of prior information, was proposed to improve the classification of fine-grained classes. Lastly, we also propose a new module based on Graph Neural Neworks, we call Graph Confidence Propagation (GCP) that additionally improves the performance of both object detection and instance segmentation modules by combining all the model outputs considering the global image context. Exhaustive quantitive and qualitative analysis performed on two open source food benchmarks, namely the UECFood-256 (object detection) and the AiCrowd Food Recognition Challenge 2022 dataset (instance segmentation) using different baseline algorithms prove the robust improvements introduced by the different components proposed in this thesis. More concretely, we outperformed the state-of-the-art performance on both public datasets
