LaksData: Comprehensive database of salmon fillets images for use in vision-based food automation applications

This article provides a salmon fillet dataset to investigate the detection of distinct regions, undesirable spots, and possibly the higher nutrient content measurements. Since we know that the belly of salmon is high in omega-3 fatty acids, we can use computer vision and image processing to identify the belly areas of salmon fillets (for trim A, B, and C cuts, trim A cut has the largest belly area) and determine the percentage of these fatty acids. As a result, this dataset becomes essential for training models that identify and examine the belly regions. Datasets were acquired from Lerøy Aurora, a salmon processing plant in Skjervøy, Norway, as well as images taken in our lab during experiments. To acquire the images at the Lerøy plant, two settings were used: (i) using a stand with 3 Intel RealSense RGB-D cameras and (ii) using a stand with 1 Intel RealSense RGB-D camera, depending on the amount of space available to put our setup near the production line. The camera equipment was positioned close to the production line. In total, 712 RGB images, 10 ROS (Robot Operating System) bags with 3 camera settings, and 5 ROS bags with 1 camera setting were taken in the Lerøy plant, while 60 RGB images were captured at the NMBU lab. ROS nodes were utilized to capture both the ROS bags (which carried RGB-D information) and the RGB images. To facilitate further research on salmon fillets, this collection also contains 509 multispectral images of fish fillets. The dataset is intended primarily as a benchmarking and pre-training resource, demonstrating the potential of computer vision for salmon fillet analysis. In conclusion, this comprehensive dataset provides a solid base for potential research on automated salmon fillet analysis. This will enable computer vision and image processing to enhance quality control and nutritional evaluation of salmon fillets.