The fry counting is essential in aquaculture. Precisely counting the fry can be used to estimate the quantity of aquaculture products. Deep-learning-based multi-object tracking and counting technologies have received increasing attention in recent years. However, detecting and tracking fry in complex scenarios remains challenging due to fry’s high density and irregular movements. To address these issues, we introduce a deformable convolutional block (DCB) to capture more fine-grained features and vertex distance intersection over union (VDIoU) loss for better bounding box regression localization. Thus, our model can efficiently deal with the high-density scenarios caused by neighboring or overlapping fry. Furthermore, we introduce the enlarged intersection over union (IoU) to address the issue of unmatched tracking caused by the irregular movements of the fry. The complementary experimental results show that our methods achieve state-of-the-art performance.

Solidago canadensis L. is a typical invasive plant that has become a significant threat worldwide and profoundly impacts local ecosystems. An unmanned aerial vehicle (UAV)-based semantic segmentation (SS) system can help in monitoring the spread and location of Solidago canadensis L. To identify the growth range of this species with greater efficiency, we employ a high-speed multispectral camera, which provides richer color information and features with limited resolution, in conjunction with a high-quality RGB camera to construct a segmentation dataset. We construct a validated UAV multispectral (UAVM) dataset comprising 3260 pairs of calibrated RGB and multispectral images. All the images in the dataset underwent semantic annotation at a fine-grained pixel level, with 12 categories being covered. In addition, other plant categories can be employed in precision agriculture and ecological conservation. Moreover, we propose a benchmark model, UAVM semantic segmentation network (UAVMNet). With the aid of the feature alignment module and the UAVMFuse module, UAVMNet efficiently integrates multispectral and high-quality RGB image information, enhancing its ability to perform semantic segmentation tasks effectively. To the best of our knowledge, this is the first model to colearn semantic representations via high-quality RGB and paired multispectral information on a UAV platform. We conduct comprehensive experiments on the proposed UAVM dataset.