Object detection for sky surveillance is a challenging problem due to having small objects in a large volume and a constantly changing background which requires high resolution frames. For example, detecting flying birds in wind farms to prevent their collision with the wind turbines. This paper proposes a YOLOv4-based ensemble model for bird detection in grayscale videos captured around wind turbines in wind farms. In order to tackle this problem, we introduce two datasets—(1) Klim and (2) Skagen—collected at two locations in Denmark. We use Klim training set to train three increasingly capable YOLOv4 based models. Model 1 uses YOLOv4 trained on the Klim dataset, Model 2 introduces tiling to improve small bird detection, and the last model uses tiling and temporal stacking and achieves the best mAP values on both Klim and Skagen datasets. We used this model to set up an ensemble detector, which further improves mAP values on both datasets. The three models achieve testing mAP values of 82%, 88%, and 90% on the Klim dataset. mAP values for Model 1 and Model 3 on the Skagen dataset are 60% and 92%. Improving object detection accuracy could mitigate birds’ mortality rate by choosing the locations for such establishment and the turbines location. It can also be used to improve the collision avoidance systems used in wind energy facilities.

A three-dimensional (3D) reconstruction method and multi-camera calibration using multiple artificial reference markers have been used for precise volumetric surveillance of fast-flying objects. The method uses a two-layer 3D reconstruction that integrates two multi-camera stereo-nodes. The fields of view of stereo nodes are directed at an acute angles to each other to provide greater coverage with the given constraints and to determine the flight characteristics of objects in 3D. The object’s flight reconstruction includes a “rough” estimation of its positions relative to selected artificial reference points in both stereo nodes separately and subsequent “refinement” of calculated positions. In this paper, we describe the proposed method and calibration technique, using a multi-camera system to measure object characteristics in 3D. The proposed method applies to volumetric surveillance in situations where it is necessary to count, track, and analyze the activities of flying objects, especially birds, using high spatial resolution.

A multi-camera dome consists of number of cameras arranged in layers to monitor a hemisphere around its center. In volumetric surveillance, a 3D space is required to be monitored which can be achieved by implementing number of multi-camera domes. A monitoring height is considered as a constraint to ensure full coverage of the space below it. Accordingly, the multi-camera dome can be redesigned into a cylinder such that each of its multiple layers has different coverage radius. Minimum monitoring constraints should be met at all layers. This work is presenting a cost optimized design for the multi-camera dome that maximizes its coverage. The cost per node and number of square meters per dollar of multiple configurations are calculated using a search space of cameras and considering a set of monitoring and coverage constraints. The proposed design is cost optimized per node and provides more coverage as compared to the hemispherical multi-camera dome.